<section id="isPasted">In order to deploy mobile robots, self-driving cars, or drones into the real world, they need to be able to observe and understand the world around them, just like humans do. PhD candidate at TU/e Daan de Geus developed algorithms for automatic image recognition that are faster and more accurate than existing models. Last Wednesday, he obtained his PhD (with honors) at the Department of Electrical Engineering.</section><section tabindex="-1">De Geus thumbs through his dissertation and shows us a photo of a street, featuring different objects such as people, vehicles, traffic posts, and traffic lights (see image below). “Mobile robots and self-driving cars need to know what’s happening around them,” he says.Among other things, they need to be able to recognize and localize objects, so they can take these into account. “This allows them to move around or toward a certain object, and maybe pick it up to carry out a task.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714637661762-csm_frankfurt_000001_030669_images_0a92c1e725.jpg" style="width: 100%px;" class="fr-fic fr-dib">Traffic in Frankfurt. Image: Daan de GeusTo make robots aware of their environment, different computer vision techniques are used. It concerns an area of research that revolves around automatic extraction of relevant information from camera footage. “Simply put, we try to make models that can distill as much information from a photo as possible,” the PhD candidate explains.“The goal of computer vision is to make a system that mimics our own visual system, allowing computers to see in the same way humans do and, as a result, interact properly with the world around them.”His research focuses on improving image recognition techniques in the area of scene understanding. This is a small, but crucial part of computer vision. “Its goal is to recognize different objects and regions in an image and to give them a semantic label, such as ‘streetlight’, ‘road’, ‘car’, or ‘person’,” he explains.In other words, the objects are given a significance that is clear to humans.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714637694452-csm_scene_understanding_example_189e7cc585.jpg" style="width: 100%px;" class="fr-fic fr-dib">An example of scene understanding. Objects (person, traffic light, car) and image regions (the road) are automatically identified, color-coded, and semantically labeled. Image: Daan de Geus<h2 id="isPasted">Accuracy and efficiency</h2>For automatic image analyses, neural networks are used – systems that learn to carry out a certain task by receiving training involving a large amount of data. By training these neural networks in a focused manner, you end up with different models that specialize in specific tasks, such as identifying all cars in an image.In the first part of his dissertation, De Geus looked at how you can improve the accuracy and efficiency of those models. “Improving one aspect is often at the expense of another. To get more accurate results, existing methods tend to lose some of their efficiency, and vice versa,” he explains.“Which makes sense, because greater accuracy often demands more computing power, which directly impacts efficiency.” The big question, therefore, is: how can you improve these aspects without having to compromise?He came up with several solutions to this conundrum. “Efficiency is important for two reasons. You want the algorithm to use as little energy as possible and to make a prediction as quickly as possible,” he says. Speed is of the essence in self-driving vehicles, as cars must be able to respond to situations that arise in a timely manner.“If the calculation takes a second, it may already be too late to take action.”Using a process known as model unification, he combined two models to create a more efficient one. “Some tasks focus on the objects in the foreground, such as cars and people; others on ‘background regions’ such as vegetation and the sky. These tasks are performed by two different neural network modules, as each module specializes in a different task,” he explains.“Using two network modules isn’t very efficient, because you have to run them parallel to each other.” He found out that you can supply extra information to the network module processing the background information, allowing it to also identify foreground objects.“As a result, the module for foreground objects is no longer necessary, which greatly enhances efficiency. This makes this model twice as fast as its precursors, while its accuracy is comparable.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714637727049-csm_image_with_patches_0ce2b38ff7.jpg" style="width: 100%px;" class="fr-fic fr-dib">Image with image regions. Image: Daan de GeusAnother way of improving efficiency is based on the observation that many regions in an image are very similar. For instance, the sky generally takes up a large part of any picture you take outside. “In spite of the fact that a lot of information is similar, neural networks process each image region separately, which is very inefficient,” says De Geus.The entire image is divided up into patches consisting of pixels (see image below). Normally, these would be labeled and assessed individually, but the PhD candidate developed a method for clustering patches with comparable information, reducing the total number of patches and, by extension, requiring less computing power.“This allows us to improve speed by as much as 110%, without taking away from the accuracy.”Incidentally, this method for grouping similar image regions is widely applicable to many different models and a great many applications other than self-driving cars and mobile robots. For example, you could use these algorithms to segment medical images. “Pretty much in any situation where you need to automatically analyze images.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714637752641-csm_token_sharing_diagram_c977af3012.jpg" style="width: 100%px;" class="fr-fic fr-dib">To improve efficiency, semantically comparable patches can be clustered. Image: Daan de Geus<h3 id="isPasted">Multiple abstraction levels</h3>In the second part of his dissertation, he focused on what he calls multiple abstraction levels. “Existing algorithms either focus on entire objects, such as cars, or on their components, such as car tires or the license plate,” he explains.His goals was to develop an algorithm that can simultaneously understand an image at multiple abstraction levels. “This will allow a mobile robot to observe both the car and its components, as well as other objects and the background, all at the same time, which gives it a comprehensive picture of the surroundings,” says De Geus.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714637778377-csm_frankfurt_000001_030669_panoptic_5abb4ba438.jpg" style="width: 100%px;" class="fr-fic fr-dib">Automatic recognition of objects and image regions. Image: Daan de GeusYou can have both algorithms carry out their work separately and merge the results afterwards, but this is cumbersome. And it can also lead to conflicts between the two calculations. Instead, he developed a new algorithm that can identify objects and components all at once.“That’s not only more accurate, but also more efficient.” What’s more, this improved model is widely applicable and has many advantages. “It means, for instance, that a robot isn’t just able to recognize a door, but also the door handle, and to understand that the latter is part of the door. Which, in turn, allows it to open the door,” he says to illustrate.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714637801563-csm_frankfurt_000001_030669_pps_3b32a5d47d.jpg" style="width: 100%px;" class="fr-fic fr-dib">A more detailed segmentation, where components of objects are also identified. Image: Daan de GeusTo be able to use self-driving cares or mobile robots in practice, there’s a lot left to do still. “Image recognition is just a tiny part of the puzzle,” he admits.&nbsp;And there are plenty of remaining challenges in the area. For one thing, models could be developed that can recognize more objects at once and that operate even faster and more accurately.The generalization of the model could also be improved, so it also produces good results when the data are different from the data sets with which the network was trained, allowing it to perform better in real-world scenarios.But at least his dissertation constitutes a step forward in making the system more capable, accurate, and efficient.<hr>Source: <a href="https://www.cursor.tue.nl/en/news/2024/april/week-3/home-stretch-teaching-robots-to-see-better/" target="_blank" rel="noreferrer">Cursor/Martina Silbrníková</a>Daan de Geusdefended his thesis“<a href="https://research.tue.nl/en/publications/characterization-of-abdominal-aortic-aneurysms-using-time-resolve" target="_blank" rel="noreferrer">Advances in Scene Understanding - Towards Efficient Image Segmentation at Multiple Abstraction Levels</a>” at the Department of Electrical Engineering on April 17th, 2024.Supervisors: Gijs Dubbelman en Peter de With</section>

In his doctoral research, Daan de Geus worked on advanced image processing methods that allow robots and cars to better recognize what they see around them.

Teaching robots to see better

<h1 id="isPasted">Wired for Autonomy: Confronting PCBA Complexities in Mobile Robotics</h1><section>The field of untethered robotics is booming, and across industries, autonomous robots are rapidly assuming responsibility for an increasing number of tasks. In their expansion into increasingly diverse industries, these robots place additional demands on their internal workings, specifically the PCBAs that help them function.However, developing PCBAs for autonomous robot systems presents several unique challenges that electrical engineers must overcome to succeed. We take a deep dive into these challenges by exploring design considerations beyond those associated with conventional PCBAs.<h3>Beyond the Basics: Design Considerations for Autonomous Robots</h3>While core electrical principles remain a top priority, autonomous robotics introduces additional complexity to PCBA design that electrical engineers must consider from day one. To function properly, these machines must anticipate and plan for the unexpected in harsh, changing conditions.Intuitive Machines’ Odysseus moon lander recently brought this to light, when the private spacecraft <a href="https://www.reuters.com/technology/space/moon-lander-described-alive-well-day-after-white-knuckle-lunar-touchdown-2024-02-23/" target="_blank">landed on the moon’s surface</a>. As much as the sideways landing attracted media attention, few reports focused on last-minute troubleshooting that kept the mission afloat, a testament to the quick-thinking nature of the engineers involved.Here are some of the important factors to consider when designing PCBAs for untethered robotics:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713979758192-1713979758192.png" alt="Autonomous robot design considerations" class="fr-fic fr-dii"><ul><li>PCBAs Must Prioritize Efficiency: Untethered robots almost entirely rely on batteries, so PCBAs must focus on efficiency. The key to this is meticulous component selection, such as using low-power microcontrollers and efficient voltage regulators, as well as excellent circuit design that minimizes standby currents. Using clock gating also maximizes battery life and reduces heat generation.</li><li>Maintain Signal Integrity Amid Noise: Robots operate in dynamic environments with <a href="https://www.macrofab.com/blog/bypass-caps-decouple-your-way-to-cleaner-power/" target="_blank">potential electrical noise</a> from motors, sensors, and external sources. EMI can wreak havoc on robotic sensors, causing glitches, calibration issues, or even complete sensor failure. Consider layout strategies in detail to cut crosstalk and ground loops and maintain clear signal transmission.</li><li>Build for Endurance: Many robots are subjected to extreme temperatures, temperature fluctuations, vibrations, and shocks. Use ruggedized PCBA boards that have components with adequate vibration and temperature ratings. To prevent overheating, soldering must be properly performed to handle thermal expansion and contraction, mechanical fatigue, and dust and moisture infiltration. Enclosures need to protect against dust and moisture egress.</li><li>Size Matters: Robots must be mobile, so PCBAs must also be compact and lightweight to minimize robot weight. Choose wisely when choosing mechanisms within your robot, like vacuum track-based models or electromagnetic ones. They may increase the mass of your robot and reduce maneuverability. High-density interconnect (HDI) techniques and minimizing wasted space become crucial.</li><li>Don’t Compromise Reliability: Autonomous robots, operating alone, demand ultra-reliable PCBAs to minimize failure. Filtering tackles this by reducing sensor noise for cleaner data, smoothing data for fewer errors, extracting key features for better navigation, and even lowering processing load for faster decision-making. Redundancy and fail-safe features further enhance reliability.</li><li>Play by the Rules: Robots must comply with <a href="https://www.macrofab.com/blog/safeguarding-pcba-design-from-electromagnetic-interference/" target="_blank">Electromagnetic Compatibility (EMC) regulations</a> to ensure proper operation and avoid interfering with other electronic devices. Shielding techniques and filtering components become necessary.</li><li>Ensure Maintainability: After assembling, it’s crucial to diagnose faults and perform maintenance. Make sure test points are conveniently accessible after the final assembly. Consider implementing Built-in Test (BIT) features on the PCBA for self-diagnostic features or options for wireless troubleshooting.</li></ul><h2> </h2><h3>The Unexpected Factors: When Biology Interferes</h3>Biological influences are often overlooked when designing PCBAs for autonomous robotics but are an important factor. Autonomous robotics may work in many different environments, encountering biological challenges that can undermine even the most robust and reliable designs.<h3>Biofouling</h3>Biofouling, or the accumulation of microorganisms on surfaces, happens most frequently in aquatic environments. However, this issue can also occur within humid industrial settings or food processing facilities. Biofouling causes issues in many ways including:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713979758511-1713979758511.png" alt="Biofouling" class="fr-fic fr-dii"><ul><li>Reduced Performance: Fouling on sensors, thrusters, and hulls can decrease efficiency and accuracy. As a result, sensors become less sensitive, propellers drag more, and overall power consumption increases. Special coatings applied to surfaces can deter organism attachment.</li><li>Problems with Navigation: Fouling on navigational equipment leads to positioning and localization errors, which make it difficult for the robot to map its surroundings, keep its trajectory, and reach its destination.</li><li>Maintenance Issues: Regular cleaning to remove biofouling is essential, particularly in remote locations. As a result, downtime occurs and reduces operational efficiency. Prevent biofouling with brushes or wipers, as well as scheduled cleaning protocols.</li><li>Damage and Disruption: Biofouling can cause physical damage by promoting barnacle growth and corroding structures. Choosing materials that discourage organism growth is a passive defense, and monitoring systems can detect biofouling early and trigger automatic cleaning.</li></ul>Especially within industrial and food processing industries, biofouling can create a surprising but challenging issue for logistics robotics in order fulfillment, for example, or other autonomous vehicles. Make sure to consider this possibility when designing for specific applications.<h3> </h3><h3>Biological Challenges Across Environments</h3>While biofouling dominates aquatic environments, other biological factors impact robots in other settings:<ul><li>On Land: Dust, dirt, and debris can accumulate on sensors and cameras in terrestrial robots, hindering performance. Agricultural robots may encounter challenges with mud and plant debris.</li><li>Airborne: Airborne robots face issues with dust, pollen, insects, and other airborne particles that can increase drag, reduce lift, and affect sensor accuracy.</li></ul>To address animal-related challenges, EEs can employ design strategies like:1. Physical Resilence:&nbsp;<ul><li>Challenge: Animals may chew on cables or antennas, causing malfunctions.</li><li>Solution: Employ ruggedized components, reinforced substrates, and protective enclosures to enhance PCBA durability against physical damage from animal interactions. Additionally, utilize encapsulation and conformal coatings to safeguard critical components from moisture, dust, and possible damage by animals.</li></ul>2. Sensor Integrity:<ul><li>Challenge: Animals brushing against sensors can distort sensor inputs, leading to navigation errors.</li><li>Solution: Design sensors with robust shielding, noise filtering capabilities, and redundancy features to ensure reliable operation and accurate data despite potential interference from animals. Develop strategies for preventing animals from brushing against sensors to keep distortions and navigational errors from happening. For example, anti-fouling paints or cages around the robot could prevent fouling, or sensors might be placed where animals cannot access them.</li></ul>3. Mobility Optimization:<ul><li>Challenge: Animals blocking pathways or obstructing movement can impede the robot’s forward progress.</li><li>Solution: Develop strategies to address mobility constraints caused by animals blocking pathways or obstructing movement, ensuring that robots can navigate effectively and complete their objectives. This could include low-power LEDs or subtle sounds that encourage animals to move away. Optimize power management systems to maximize operational autonomy and extend battery life in resource-constrained environments where robots may interact with animals.</li></ul><h3>Security: The Next Level in AMR PCBA Design</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713979758575-1713979758575.png" alt="AMR PCBA security" class="fr-fic fr-dii">AMR PCBA securityPCB design traditionally focuses on protecting against electrical malfunctions and physical damage. However, for AMRs (autonomous mobile robots) connected to the internet, cybersecurity emerges as a key concern. Hacking hazards present significant security risks. A compromised robot can open the door to major security breaches. Thus, incorporating encryption and secure boot mechanisms in PCBA design is essential to block unauthorized access.Hardware Trojan Horses also pose a silent yet profound threat. These sneaky modifications, made during manufacturing, can go almost unnoticed. To counteract this, it’s critical to partner with trustworthy suppliers and employ hardware authentication methods.Consider working with a partner with a connected technology platform that keeps you in touch with your manufacturing process from start to finish. Moreover, work with a partner that employs robust security practices and can provide proof of such, such as ISO and ITAR certifications, or membership in the ERAI, an organization established to <a href="https://www.macrofab.com/blog/electronics-industry-counterfeit-parts-problem/" target="_blank">combat counterfeit electronics</a> and high-risk components in the electronics industry. MacroFab holds these standards, and it is a member of ERAI.Another challenge is the theft of PCBAs after robot deployment, especially those containing valuable intellectual property. Consider using tamper-evident seals or protective enclosures if you will be installing robotics in an unsupervised area.Want to read more about designing PCBAs for robotics? Read <a href="https://www.macrofab.com/documents/reshaping-robotics-how-pcba-advances-drive-tomorrows-autonomous-solutions/" target="_blank">Reshaping Robotics: How PCBA Advances Drive Tomorrow's Autonomous Soutions</a> now.<h2> </h2><h3>Conclusion: Embracing the Challenge</h3>The realm of PCBA design for autonomous robots transcends the traditional electrical engineer’s domain. While electrical fundamentals remain the cornerstone, navigating the complexities of this field necessitates venturing beyond the confines of the schematic. It’s a world where understanding environmental stresses, mitigating biological influences, and safeguarding against security vulnerabilities are equally critical.By embracing these unique challenges and fostering a spirit of relentless innovation, electrical engineers will become the architects of a new era in robotics. The future holds immense potential for these autonomous machines, with robust, adaptable PCBAs serving as their foundation.Need PCBAs for your robotics project?&nbsp;</section><a href="https://factory.macrofab.com/quick_quote?mbsy_source=c2dfbfd3-8c8b-4098-87b7-c7e0e06119ec&campaignid=62990&mbsy=6DBV3m" target="_blank">GET AN INSTANT QUOTE NOW</a><section><h5> </h5></section>

Autonomous robotics must anticipate and plan for the unexpected, often in harsh, changing environments. We take a deep dive into these challenges by exploring design considerations beyond those associated with conventional PCBAs.

Wired for Autonomy: Confronting PCBA Complexities in Mobile Robotics

In this episode, we discuss research coming out of George Mason University where the RobotiXX lab has been training robots to drive more like humans

Podcast: Robot That Drives Better Than The Average American

<blockquote>“For a new product to be successful, it is not enough for it to work—almost everything around us works quite well. The design matters.”</blockquote>This is what Iván Arakistain, a technologist and researcher at Tecnalia said. After winning the Connect for Good challenge, we interviewed Arakistain to discuss his award-winning project on sustainable logistics infrastructure and his insights into the logistics industry, particularly that of perishable goods.Logistics is a dynamic field fraught with challenges, and sustainability is a growing concern, particularly in transporting perishable goods. Iván Arakistain, representing Tecnalia, won first place in the Connect for Good challenge with an innovative concept that leverages a low-power IoT solution to monitor and enable eco-friendly transport of perishable goods.The&nbsp;<a href="https://www.wevolver.com/article/connect-for-good-low-power-wireless-sustainability-challenge" target="_blank">Connect for Good Challenge</a> is a joint initiative by Nordic Semiconductor, Mouser Electronics, Soracom, and Crowd Supply aimed at fostering innovation for social and environmental good. It brought together a worldwide network of engineers, entrepreneurs, and developers around one mission: “to devise low-power wireless solutions that contribute towards achieving the United Nations Sustainable Development Goals.”&nbsp;Arakistain's winning project is a game-changer in sustainable logistics. Through advanced monitoring and anomaly detection, this innovative solution optimizes the transport of perishable goods while eliminating the need for energy-intensive refrigerated cargo trailers. Thus, it significantly reduces carbon emissions and operational costs while ensuring the integrity of the cold chain. Through the integration of IoT technology, including low-energy sensors and GPS and coupled with mini solar panels for autonomy, the project sets a new standard for eco-friendly transport in the logistics industry, with broad implications for global sustainability efforts. The key component of this project is the integration of the&nbsp;<a href="https://www.wevolver.com/specs/nrf9160-development-kit" target="_blank">nRF9160 Development Kit</a>, chosen specifically for its low energy consumption and reliable cellular connectivity.In this exclusive interview, Arakistain sheds light on his journey, the inspiration behind his project, and the impact he envisions on the logistics industry's future.<h2 dir="ltr">Interview with Iván Arakistain of Tecnalia</h2><h3 dir="ltr">Can you briefly describe your winning project and the main problem it addresses in the logistics industry?</h3>One of the main challenges of the logistics industry is the sustainable transport transition. This focuses on decarbonizing the transport industry, especially because today, at least in Spain, it’s heavily based on trucks; most of the logistics happen on the road, and trucks consume a lot of fuel. So, we have to shift from that to a cleaner way of transport.Some years ago, we had a customer who sold precooked food.&nbsp;They mentioned something to us that sparked this idea process: If they could deliver the food in one day from their factory to the end customer, they would be able to avoid refrigerated transportation, especially since it’s relatively expensive, requires a lot of fuel, and is very complex.So, we started exploring that. At that time, the technology was not quite ready for the application, mainly for areas like the south of Spain, where it's very hot in the summer. The logistics were not as fast as today, but as things have evolved, we are now able to transport food—or perishable goods—in proper condition to the end customer in a short a time as needed so it arrives safely.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713160590293-0_Ivan+Arakistain.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h3 dir="ltr">What inspired you to focus on developing a more sustainable method for transporting perishable goods?</h3>Well, the main goal is to deliver goods fast and with as low carbon emissions as possible. For that, we must change from only using transport by trucks to combining truck and train, which is obviously much cleaner environmentally. By doing so we can ensure more availability, lower cost per kilometer, and a smaller cargo footprint.&nbsp;Let’s look at how your project aligns with the Sustainability Development Goals. Could you tell us how your solution targets these goals?Our solution’s practical implications impact goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 13 (Climate Action). We are very focused on energy efficiency and sustainability goals for cities and districts. Our application can help reduce the cargo footprint and accelerate the transition from the current fuel-based transport to a more electrified way of transport.&nbsp;Of course, we cannot transition directly from 0 to 100%, so we must ensure the transition is smooth. In our area of focus, the technology background is in artificial intelligence for microcontrollers and small devices. We embed this intelligence into the embedded device so that we can know via our electronic sensors if the transported food is in good condition. We can also sense whether any volatile compounds can impact the condition of the food.By making those changes, we estimate that up to 10% of carbon reduction is possible. And that also applies to energy consumption, even though I’m not sure if it’s a linear relationship. However, the estimation stands.<h3 dir="ltr">Let's move onto the technical side. How does your solution work? Is embedded AI the differentiator from traditional energy-intensive refrigeration?</h3>As I said, we are focused on AI for microcontrollers. So, first, we develop the AI models in servers and powerful computers. Then, we reduce those models so that they can run in a small controller with a few kilobytes or a small RAM. For this application, we are focused on two types of inference.The first type of inference is air quality to determine the state of the product. The second inference type is AI for vibration analysis, which is something we have implemented here and in other projects. In this case, we can analyze if the vibration of products in the truck matches the orientation to make sure everything is good. In other cases, we also do this AI analysis of vibration for structural health and many other applications.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713160621737-1_Manufactured+PCB+Top.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h3 dir="ltr">How accurate and precise are the AI model results in a real-world application?&nbsp;</h3>There are two processes that we do. First, we collect all the data, and then we perform model training and model testing on our computers. We split the data set into two parts: about 80% of the data is used for training the model, and the remaining 20% or so is used for testing the model.In our computer simulation, the results were very good; you can get up to 96% accuracy. But we both know that’s not the reality. Getting a very high score virtually means the model is quite good. However, when you go into the real world and flash the firmware into the microcontroller you’re using in the actual sensor, the accuracy decreases a bit. Still, we can get good enough accuracy with this model.Were there any particular challenges you faced in the development process, and how did you overcome them?As I said earlier, the original idea came some years ago with this customer we were working with. They produced precooked food, and we did some real testing with them. We sent the food with a trucker and other things to actual customers. At that moment, we realized that the technology was not quite ready, not because of the electronics but because of the isolation and insulation challenges. For that, you’d need excellent isolating materials so that temperatures are kept in the correct range.&nbsp;This was about eight to nine years ago. At that point, we did develop a complete solution, and we knew the idea was good, but it was not quite ready because of the isolation challenge. Since then, we have kept thinking and working on it, and now we know that it is a feasible application, especially since there are much more efficient containers for food transport than years ago. There are better isolation materials, which helped this improve.<h3 dir="ltr">Looking into the next decade, what are the biggest hurdles in scaling and implementing your solution across different regions and logistics networks?</h3>The main difficulty is the limited time for delivery: It has to be done within the first 24 to 48 hours and the products need a low temperature range.Of course, I think it's scalable. Still, one of the biggest challenges is the container. We need to use special containers with good insulation for this. Basically, once we are able to prove to customers that they are going to save so much on each transportation, then it makes sense.So far, we have tested with first-class precooked food, which is already prepared and ready for eating upon reheating in a restaurant or somewhere else. We are not so focused on uncooked food.<h3 dir="ltr">Let’s dive a bit into the economic side of things. What are the economic benefits of your solution for logistics companies and end users?</h3>The economic benefits mainly rely on the use of nonrefrigerated transportation. As a result, you can save about 10% of the transportation cost. It consumes less fuel and energy. It's simply more efficient. We are also very interested in combining ways of transportation: using not only trucks but also trains. Here in Spain, we have quite a robust train infrastructure, but nowadays, it is more focused on passengers than goods. So, I think we must leverage the train infrastructure and combine it with trucks in the future.Also, for international shipments, combining the truck with the train is important so that the driver does not need to drive so far. The truck driver would simply mount the truck with the goods from a train carriage, move onto another carriage to collect goods from there, and then deliver them locally.This is a standard logistics process. We are simply using the existing infrastructure. The only additional requirement would be the special containers for the food that I mentioned earlier.<h3 dir="ltr">You mentioned one customer already.&nbsp;Have you discussed this with other members of your target market? How have your target customers responded to your solution so far?</h3>Yes. 15 days ago, I was at a logistics and transportation fair in Bilbao. I gave a speech there and talked to potential customers. Clearly, there is some potential, but of course, we need to keep the focus on the end customer.<h3 dir="ltr">By focusing on the end customer, what strategies are you employing to encourage the adoption of your technology within the market?&nbsp;</h3>In Tecnalia, we work with different approaches to ensure the technology adoption. We identify and manage deep-tech business opportunities, through a venture building process to accelerate their industrial commercialization. We create new startups or commercialize licenses, accelerating the incubation of technological assets until they are turned into investment opportunities capable of generating economic and social value.&nbsp;<h3 dir="ltr">How do you see your solution evolving in the next five years?</h3>I think that it has quite a lot of potential. We are still in a transition stage, where not everything is well-defined. The path is clear: we have to shift to the electrification of transport. But the speed at which this is going to happen is yet to be clear. Yet, I think that it has significant potential for the future because logistics is a fast-growing sector.<h3 dir="ltr">Is there close competition to your technology from bigger companies already established in the logistics market?</h3>Yes, there is. For example, some years ago, big companies like Amazon tried to sell perishable food to end customers in a very short time. However, they were not very successful because it’s not easy to deliver that fast in a cost-efficient way.Our case is a little different. It focuses on precooked food for industrial partners. The customer I mentioned earlier is not selling to end customers but rather to restaurants and other businesses. So yes, there are big players, but they are not entirely in the same market niche as ours.<h3 dir="ltr">Are there other applications or industries where your technology could be applied beyond the niche cold chain logistics?</h3>Yes, we use embedded AI for structural health monitoring in various cases. Here, you would need to know the condition of a structure. In Tecnalia, for example, we have some dedicated lab equipment, such as sensitive accelerometers. But the cost of such devices is typically quite high. They are not devices you can leave on a structure but rather devices you use within a specific timeframe.So, we are developing small, affordable devices that can use MEMS accelerometers, inexpensive microcontrollers, and embedded AI to provide early warnings if there is an anomaly, failure, or any other event on which the model is trained.The new devices we are developing would cost as much as an order of magnitude less than the typical measuring and monitoring equipment. They would only provide an early warning, after which the experts could go and perform more extensive measurement and testing.Another example of our embedded AI would be audio detection. Consider a large dataset of street sounds that can take over six gigabytes – that is a lot of information. For this, it's possible to train an AI model that takes no more than kilobytes – which is significantly less – and embed it into a microcontroller to classify the sounds. We tested this, and the model was able to accurately identify and classify the sounds of a car horn, for example, a dog barking or children playing. This is a solution to tackle problems with lots of data using a compressed model that can accurately classify the data without requiring so much power.<h3 dir="ltr">Going back to the project experience, what have you learned from working on this project that you think is crucial for innovation in sustainable technologies?</h3>At Tecnalia, we are experts in many different fields. The most interesting thing about working in an applied research and technological development centre like Tecnalia is that I can collaborate with experts in multiple fields, such as electronics, logistics, structures, and energy efficiency. For such projects to work, I have to work with lots of people to solve the problem.&nbsp;Also, experts in AI are not all the same. Some of them are more focused on big processors like NVidia devices, and we need to collaborate with them to understand the models and be able to quantize and reduce the models down to the microcontroller level.What advice would you give to other entrepreneurs or innovators looking to make a difference in the green technology space?&nbsp;My advice would be to prototype as much as they can. In electronics, we are quite used to prototyping fast. That's a good thing for our sector. But in other areas, it might be more difficult to prototype. I would also encourage people to collaborate with other experts and learn from them.<h2 dir="ltr">Conclusion</h2>Iván Arakistain's winning project exemplifies the potential for low-power IoT solutions to revolutionize the transportation of perishable goods. By leveraging advanced monitoring and anomaly detection, Arakistain's project offers a promising avenue for reducing carbon emissions and operational costs while maintaining the integrity of the cold chain.&nbsp;As we look to the future, the scalability and adoption of such solutions present ongoing challenges. Yet the momentum generated by initiatives like the Connect for Good Challenge signals a promising trajectory towards more innovation and sustainable solutions and systems. For more information about the challenge and its winners, check out our dedicated article:&nbsp;<a href="https://www.wevolver.com/article/connect-for-good-challenge-winners-logistics-infrastructure-monitoring-smart-bees-and-bioreactors" target="_blank">Connect for Good Challenge Winners: Logistics Infrastructure Monitoring, Smart Bees and Bioreactors</a>.

Introducing the Winner of the Connect for Good Challenge 2023


Towards Sustainable Logistics Infrastructure: An Interview with Tecnalia

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/2b4595aa-c087-4a18-9ed9-6c05655462a7?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Picture this: You’re out for a drive and in a hurry to reach your destination.At first, the road is clear and dry. You’ve got great traction and things are going smoothly.&nbsp;But then the road turns to gravel, with twists and turns along the way. You know your vehicle well, and have navigated such terrain before.And so, instinctually, you slow the vehicle to navigate the more challenging conditions. By doing so, you avoid slipping on the gravel. Your experience with driving, and in detecting the conditions, has saved you from a potential mishap. Yes, you slowed down a bit. But you’ll speed up again when the conditions improve. The same scenario could apply to driving on grass, ice – or even just a hairpin corner on a dry paved road.For human beings, especially those with years of driving experience, such adjustments are second-nature. We have learned from experience, and we know the limitations of our vehicles. We see and instantly recognize potentially hazardous conditions – and we react.But what about if you’re a robot? Particularly, a robot that wants to reach a destination at the maximum safe speed?That’s the crux of fascinating research taking place at <a href="https://www.gmu.edu/" target="_blank">George Mason University</a>: Building robots that are taught – and can subsequently teach themselves – how to adapt to changing terrain to ensure stable travel at the maximum safe speed.It’s very cool research, with really positive implications.Below: You don’t want this happening on a critical mission…<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyNTk4MjUwMzM0LVBpY3R1cmUyLnBuZy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 328px;" class="fr-fic fr-dib" alt="research platforms"> <h3 id="isPasted">“XX”</h3>Those are the initials of Dr. Xuesu Xiao, an Assistant Professor at George Mason University. He holds a PhD in Computer Science, and runs a lab that plays off his initials, called the <a href="https://cs.gmu.edu/~xiao/RobotiXX/lab.html" target="_blank">RobotiXX Lab</a>. Here’s a snippet of the description from his website:“At RobotiXX lab, researchers (XX-Men) and robots (XX-Bots) perform robotics research at the intersection of motion planning and machine learning with a specific focus on robustly deployable field robotics. Our research goal is to develop highly capable and intelligent mobile robots that are robustly deployable in the real world with minimal human supervision.”We spoke with Dr. Xiao about this work.It turns out he’s particularly interested in making robots that are particularly useful to First Responders, and carrying out those dull, dirty and dangerous tasks. Speed in such situations can be critical, but comes with its own set of challenges. A robot that makes too sharp a turn at speed on a high friction surface can easily roll over – effectively becoming useless in its task. Plus, there are the difficulties previously flagged with other terrains.This area of “motion planning” fascinates Dr. Xiao. Specifically, how to take robots beyond traditional motion planning and enable them to identify and adapt to changing conditions. And that involves machine vision and machine learning.“Most motion planners used in existing robots are classical methods,” he says. “What we want to do is embed machine learning techniques to make those classical motion planners more intelligent. That means I want the robots to not only plan their own motion, but also learn from their own past experiences.”In other words, he and his students have been focussing on pushing robots to develop capabilities that surpass the instructions and algorithms a roboticist might traditionally program.“So they’re not just executing what has been programmed by their designers, right? I want them to &nbsp;improve on their own, utilising all the different sources of information they can get while working in the field.”&nbsp;<h3>The Platform</h3>The RobotiXX Lab has chosen the <a href="https://indrorobotics.ca/hunter-se/" target="_blank">Hunter SE</a> as its core platform for this work. That platform was supplied by InDro Robotics, and modified with the <a href="https://indrorobotics.ca/commander/" target="_blank" rel="noopener noreferrer"></a><a href="https://indrorobotics.ca/commander/" target="_blank" rel="noopener noreferrer">InDro Commander module</a>. That module enables communication over 5G (and 4G) networks, enabling high speed data throughput. It comes complete with multiple USB slots and the Robot Operating System (ROS) library onboard, enabling the easy addition (or removal) of multiple sensors and other modifications. It also has a remote dashboard for controlling missions, plotting waypoints, etc.Dr. Xiao was interested in this platform for a specific reason.“The main reason is because it’s high speed, with a top speed of 4.8m per second. For a one-fifth/one-sixth scale vehicle that is a very, very high speed. And we want to study what will happen when you are executing a turn, for example, while driving very quickly.”As noted previously, people with driving experience instinctively get it. They know how to react.“Humans have a pretty good grasp on what terrain means,” he says. “Rocky terrain means things will get bumpy, grass can impede a motion, and if you’re driving on a high-friction surface you can’t turn sharply at speed. We understand these phenomenon. The problem is, robots don’t.”So how can we teach robots to be more human in their ability to navigate and adjust to such terrains – and to learn from their mistakes?As you’ll see in the diagram below, it gets *very* technical. But we’ll do our best to explain.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyNTk4NDM4NTk4LUdlb3JnZS1NYXNvbi1qcGcud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">The Approach</h3>The basics here are pretty clear, says Dr. Xiao.“We want to teach the robots to know the consequences of taking some aggressive maneuvers at different speeds on different terrains.&nbsp;If you drive very quickly while the friction between your tires and the ground is high, taking a very sharp turn will actually cause the vehicle to roll over – and there’s no way the robot by itself will be able to recover from it, right?&nbsp;So the whole idea of the paper is trying to enable robots to understand all these consequences; to make them ‘competence aware.'”<a href="https://cs.gmu.edu/~xiao/papers/cahsor.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">The paper</a> Dr. Xiao is referring to has been submitted for scientific publication. It’s pretty meaty, and is intended for engineers/roboticists. It’s authored by Dr. Xiao and researchers Anuj Pokhrel, Mohammad Nazeri, and Aniket Datar. It’s entitled: CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in SE(3).That SE(3) term is used to describe&nbsp;how objects can move and rotate in 3D space. Technically, it stands for Special Euclidean group in three dimensions. It refers to keeping track of an object in 3D space – including position and orientation.We’ll get to more of the paper in a minute, but we asked Dr. Xiao to give us some help understanding what the team did to achieve these results. Was it just coding? Or were there some hardware adjustments as well?Turns out, there were both. Yes, there was plenty of complex coding. There was also the addition of an RTK GPS unit so that the robot’s position in space could be measured as accurately as possible. Because the team soon discovered that intense vibration over rough surfaces could loosen components, threadlock was used to keep things tightly in place.But, as you might have guessed, machine vision and machine learning are a big part of this whole process. The robot needs to identify the terrain in order to know how to react.We asked Dr. Xiao if an external data library was used and imported for the project. The answer? “No.”“There’s no dataset out there that includes&nbsp;all these different basic catastrophic consequences when you’re doing aggressive maneuvers.&nbsp;So all the data we used to train the robot and to train our machine learning algorithms were all collected by ourselves.”&nbsp;<h3>Slips, Slides and Rollovers</h3>As part of the training process, the Hunter SE was driven over all manner of demanding terrain.“We actually bumped it through very large rocks many times and also slid it all over the place,” he says. “We actually rolled the vehicle over entirely many times. This was all very important for us to collect some data so that it learns to not do that in the future, right?”And while the cameras and machine vision were instrumental in determining what terrain was coming up, the role of the robot’s Inertial Measurement Unit was also key.“It’s actually multi-modal perception, and vision is just part of it. So we are looking at the terrain using camera images and we are also using our IMU. Those inertial measurement unit readings sense the acceleration and the angular velocities of the robot so that it can better respond,” he says.“Because ultimately it’s not only about the visual appearance of the terrain, it is also about how you drive on it, how you feel it.”&nbsp;<h3>The Results</h3>Well, they’re impressive.The full details are outlined in <a href="https://cs.gmu.edu/~xiao/papers/cahsor.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">this paper</a>, but here’s the headline: Regardless of whether the robot was operating autonomously heading to defined waypoints, or whether a human was controlling it, there was a significant reduction in incidents (slips, slides, rollovers etc.) with only a small reduction in overall speed.Specifically, “CAHSOR (Competence-Aware High-Speed Off-Road Ground Navigation)&nbsp;can efficiently reduce vehicle instability by 62% while only compromising 8.6% average speed with the help of TRON (visual and inertial Terrain Representation for Off-road Navigation).”That’s a tremendous reduction in instability – meaning the likelihood that these robots will reach their destination without incident is greatly improved. Think of the implications for a First Responder application, where without this system a critical vehicle rushing to a scene carrying medical supplies – or even simply for situational awareness – might roll over and be rendered useless. The slight reduction in speed is a small price to pay for greatly enhancing the odds of an incident-free mission.“Without using our method, a robot will just blindly go very aggressively over every single terrain – while risking rolling over, bumps and vibrations on rocks, maybe even sliding and rolling off a cliff.”What’s more, these robots continue to learn with each and every mission. They can also share data with each other, so that the experience of one machine can be shared with many. Dr. Xiao also says the learnings from this project, which began in January of 2023, can also be applied to marine and even aerial robots.For the moment, though, the emphasis has been fully on the ground. And there can be no question this research has profound and positive implications for First Responders (and others) using robots in mission-critical situations.Below: The Hunter SE gets put through its paces. (All images courtesy of Dr. Xiao.)<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyNTk4NTAwNzQ4LWh1bnRlcjItanBnLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">InDro's Take</h3>We’re tremendously impressed with the work being carried out by Dr. Xiao and his team at George Mason University. We’re also honoured to have played a small role in supplying the Hunter SE, InDro Commander, as well as occasional support as the project progressed.“The use of robotics by First Responders is growing rapidly,” says InDro Robotics CEO Philip Reece. “Improving their ability to reach destinations safely on mission-critical deployments is extremely important work – and the data results are truly impressive.“We are hopeful the work of Dr. Xiao and his team are adopted in future beyond research and into real-world applications. There’s clearly a need for this solution.”If your institution or R&amp;D facility is interested in learning more about InDro’s stable of robots (and there are many), please reach out to us <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank">here</a>.

Empowering Robots with Adaptive Terrain Navigation

George Mason University Researchers Enable Robots to Intelligently Navigate Challenging Terrain

<h3 id="isPasted">Who are we?&nbsp;</h3>Team Polar is an Eindhoven-based student team filled with students from different disciplines. Our mission is to facilitate and improve the research going on in Antarctica. We aim to achieve this by building an autonomous, affordable, and electric rover. The reason we go with Antarctica is that the current research going on is led by human-driven vehicles using fossil fuels. Building an autonomous vehicle means it is safer and less expensive since there is no&nbsp;driver to pay.&nbsp;&nbsp;<h3>The Ice Cube&nbsp;</h3>After working for more than a year, Team Polar came up with the&nbsp;Ice Cube. Ice Cube was just the first step towards our vision of building a sustainable and affordable research environment in Antarctica. Ice Cube has 2 square meters of&nbsp;solar panels&nbsp;which helps Ice Cube to go up to 65 kilometers (about half the distance from Washington, D.C. to New York City) per day. With its 28' wheels, the Ice Cube weighs around 350 kilograms (about 771.62 lb).&nbsp;&nbsp;<h3>Progress on Ice Cube&nbsp;</h3>Team Polar is&nbsp;in&nbsp;the second prototype's design process, and the Ice Cube is only used for testing.&nbsp;Since the Ice Cube was built in&nbsp;a short time, some features were missing, such as autonomy.&nbsp;Our software team is using&nbsp;the Ice Cube&nbsp;to test their software and the new sensors&nbsp;that&nbsp;we are aiming to implement on our second prototype.&nbsp;While these tests are&nbsp;being made, new structural components are also being tested. Such as gas springs, hinges, handles, etc.&nbsp;Meanwhile, we are developing a force balance that helps&nbsp;determine&nbsp;possible ranges&nbsp;of mass of the next vehicle. We&nbsp;are also looking into the location of research, which will help&nbsp;determine&nbsp;the&nbsp;driving range and strategy for the energy balance (which will&nbsp;probably be&nbsp;made next year).&nbsp;<h3>Processing REMA Image&nbsp;</h3>Under the “Autonomous Department”, our engineers successfully developed and implemented code to process &nbsp; Reference Elevation Model of Antarctica (REMA) images.&nbsp;REMA provides the first, high-resolution (2-meter) terrain map of&nbsp;nearly the&nbsp;entire continent. Since each REMA grid point has a timestamp, any past or future point observation of elevation provides a measurement of elevation change.&nbsp;REMA may provide corrections for a wide range of remote sensing processing activities, such as image orthorectification, and interferometry, and provide constraints for geodynamic and ice flow modeling, mapping of grounding lines, and surface processes. REMA also provides a powerful new resource for field logistics planning.&nbsp;This is an important step for the autonomy of the Ice Cube. The goal is to calculate the altitudes in Antarctica to find out the clearest and most optimized path for the rover.&nbsp;&nbsp;The next step for these images is to transform the processed REMA images into a format that is compatible with our A* algorithm.&nbsp;&nbsp;<h3>A* Algorithm&nbsp;</h3>The A* search algorithm is one of the best techniques used in path-finding and graph traversals. The path we derived will be the shortest of all the other options or the most efficient by all parameters considered. &nbsp;A* works by making a lowest-cost path&nbsp;<a href="https://brilliant.org/wiki/trees-basic/" target="_blank" rel="noreferrer noopener">tree</a>&nbsp;from the start node to the target node. What makes A* different and better for many searches is that for each node, A* uses a function f(n) that gives an estimate of the total cost of a path using that node.The environment of Antarctica and the conditions are the hardest part of our project. We need to come up with innovative and technical solutions. Understanding the altitudes and general geography of Antarctica is crucial.&nbsp;&nbsp;Furthermore, the design of the rover plays a huge role in our path to success. Our Dynamics Sub team is trying to come up with new ideas for the newest version of the Ice Cube.&nbsp;&nbsp;<h3>Suspension&nbsp;</h3>Unlike the Ice Cube, the new vehicle will use a suspension system. The test in Norway in 2022 showed that; the main purpose of having a suspension is important for keeping all wheels on the ground, for improved traction. Also, it will dampen vibrations to keep the vehicles' most important components as stable as possible.&nbsp;&nbsp;The suspension system the new vehicle will be using is a so-called double wishbone suspension. For this suspension, every wheel is connected to two-wishbone-shaped arms. Every wheel is independently controlled.&nbsp;&nbsp;The main advantage of the double-wishbone configuration is its good ratio of kinematic versatility vs. complexity, it being slightly more complex than other systems like the McPherson strut and slightly simpler than others like multi-link suspensions.&nbsp;&nbsp;<h3>Ground pressure&nbsp;</h3>Getting the right ground pressure is extremely crucial for the environment the vehicle will operate. A ground pressure that is too high will cause the vehicle to get stuck in the snow or even fall into crevasses. To prevent this, the ground pressure that will be aimed for in the new vehicle is 0.1 bar. This value is also used by Artic Trucks or other snow vehicles such as the tracked vehicles made by Mattracks. In comparison, the average human exerts a pressure of 0.17 bar on the ground.&nbsp;The low ground pressure is achieved by maximizing the contact area using large and not fully inflated&nbsp;tires.&nbsp;Next Steps?&nbsp;

Facilitating the human-led and autonomous research in the Antarctic continent.

Pioneering Autonomous Antarctic Research

At recent 2023 Consumer Electronics Show (CES), Sony and Honda revealed their first electric vehicle prototype, which is equipped with 45 cameras and sensors, as well as Electric Rearview Mirrors. Electronic rearview mirrors have already made an appearance on roads in many countries. A question has been raised: will traditional rearview mirrors eventually become obsolete in smart cars? In this article, we will delve into this inquiry.<h2 dir="ltr">Transform from Light to Electronics</h2>The rearview mirror was invented in 1911 by Elmer Berger, providing drivers with an easier way to view what was behind them while driving. Modifications have been made over the years, including the “day/night” switchable mirror and the “automatic dimming” mirror, making driving safer and more comfortable.Electronic rearview mirrors have emerged in recent years in both commercial and passenger vehicles. However, it should be noted that they have not yet been widely adopted in automobile, let alone replacing traditional rearview mirrors.<h2 dir="ltr">No Wonder It’s Getting Popular</h2>Electronic Rearview Mirror is a new type of “indirect vision device”, also known as the Camera-Monitor System (CMS).The electronic rearview mirror consists of two parts: the “electronic internal rearview mirror” (also known as “streaming media rearview mirror”) is a display screen installed inside the car, and the “electronic external rearview mirror” corresponds to the traditional external rearview mirror on cars. Compared to traditional rearview mirrors, electronic rearview mirrors have three advantages.1. &nbsp; &nbsp; &nbsp;Smaller structure and lower air resistanceIn August 2022, Elon Musk stated on social media that electronic side mirrors on electric cars can reduce energy consumption by about 5%. Since an electronic side mirror is just a camera, its size can be reduced to one-third of a traditional side mirror, or even smaller. From a professional aerodynamics perspective, a smaller structure means a smaller frontal area of the vehicle, which reduces air resistance, reduces wind noise, and to some extent, decreases fuel consumption for combustion engine vehicles and increases the driving range for electric vehicles.2. &nbsp; &nbsp; &nbsp;Less affected by natural factorsWith advancements in camera display and artificial intelligence technology, the solution of “external camera + internal display” can significantly improve the adverse effects of natural conditions, such as rainwater covering side mirrors, fogging, insufficient night lighting, and other problems that plague drivers. In front of electronic side mirrors, these are “child’s play”.3. &nbsp; &nbsp; &nbsp;Smaller blind spotThis is the most intuitive advantage of electronic side mirrors. The curvature, shape, and size of traditional optical reflective mirrors are fixed, resulting in a limited field of view. Electronic side mirrors can use cameras at different angles, providing drivers with a wider field of view. Especially for large commercial vehicles, blind spots are often a fatal area.Electronic rearview mirrors also provide assistance for promotion of car intelligence. Firstly, electronic side mirrors enrich the content of intelligent cockpits. Secondly, it serves as sensors for future autonomous driving systems.<h2 dir="ltr">Not Easy to Get On Board</h2>Although electronic rearview mirrors have many advantages, they also have some limitations. The high cost is a significant drawback, and the stability requirement is high for driving safety. Furthermore, drivers may have difficulty adjusting to the new display screen after using traditional rearview mirrors for over 100 years.But the industry is optimistic that with the decrease in cost and improvement in stability of electronic rearview mirrors, more car companies will embrace electronic rearview mirrors to enhance the differentiation of their models in the process of intelligentization. Though, consumers’ attitudes remain to be seen.<h2 dir="ltr">Order the Parts that Drive Smart Cars Go Faster</h2>“Mirror, mirror on the car, who's the most reliable of them all - traditional or electronic rearview mirrors?” Although there is no definite answer to this question yet, the ongoing process of intelligentization traditional gas-powered vehicles and electric cars is transforming the automotive industry.At RPWORLD, we provide manufacturing services that include CNC machining, injection molding, sheet metal fabrication, and finishing options to help you accelerate your smart drive, faster and more cost-effectively. We have extensive experience in manufacturing a wide range of auto parts for various automotive clients, including:·&nbsp; &nbsp;&nbsp;Exterior parts: head lamp, bumper, radiator, grilles·&nbsp; &nbsp;&nbsp;Interiors parts: EPB, door panels, instrument panels· &nbsp; &nbsp;Electronic parts: battery packs, charging stations, voltage motor

At recent 2023 Consumer Electronics Show (CES), Sony and Honda revealed their first electric vehicle prototype, which is equipped with 45 cameras and sensors, as well as Electric Rearview Mirrors.

Mirror, Mirror on the Car, Who's the Most Reliable of Them All?

What could small robots on earth have to do with exploration on the moon?Quite a lot, as it turns out. Professors and engineering students at Polytechnique Montréal have been busy writing algorithms and running experiments with robots and drones with one goal in mind: To enable them to explore unfamiliar and even hostile surroundings far beyond the reach of GPS or other forms of precision location technology.“What we want to do is to explore environments including caves and surfaces on other planets or satellites using robotics,” explains Dr. Giovanni Beltrame (Ph.D.), a full professor at Polytechnique’s Departments of Computer Engineering and Software Engineering.Before we get to the how, let’s address the why.“Caves and lava tubes can be ideal places for settlement: They can be&nbsp;sealed and provide radiation shielding. There’s also a chance of finding&nbsp;water ice in them,” says Dr. Beltrame.Of course, it’s also less risky – and less expensive –&nbsp;to send robots to other planets and moons rather than human beings. They don’t require life support, don’t get tired (with the exception of having to recharge), and they can gather and process data quickly.Just think of all the data that’s been acquired on Mars by the twin Rovers and the Mars helicopter.Below: A selfie taken by NASA’s Perseverance rover November 1, 2023, during the the 960th Martian day of its mission. The rover was built with a focus on astrobiology, searching for signs of ancient microbial life on the red planet. Image courtesy of NASA.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2NTYxNzQ0OTYwLVBJQTI2MjAyLWpwZy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="NASA's Perseverance"><h3 id="isPasted">Prepare on Earth, Deploy in Space</h3>It’s a pretty ambitious vision. But for Beltrame and his team, it’s also very real. And it requires a lot of work and research here on earth.“So to get there (space) and do this with&nbsp;multiple robots, we’ve developed all sorts of technologies –&nbsp;navigation, perception, communication, coordination between the robots, and human-robot interfaces,” he says.“We’re doing all these things,&nbsp;because our goal is to use a swarm of robots to do planetary exploration. There’s more, but that’s it in a nutshell.”When you go to the moon, there’s no equivalent of GPS. And environments like caves can be really tricky – both in terms of robots understanding where they are, and also communicating with other robots beyond line of sight.With the right technologies and algorithms, that communication is possible. And much of Beltrame’s research has involved testing this on earth. In particular, he’s focusing on how groups of robots could take on such tasks collaboratively.“So our primary activities focus on swarm robotics,” he says.Generally that starts with simulation models. But there are limits to simulations – and real-world testing is a big part of what’s going on at Polytechnique.“So we do have this deployment philosophy that we try our technologies in simulation, but then we want to go to deploying robots. You can have the best simulation in the world, but there’s still a reality gap and it’s very extremely important to try things on the real robots,” he says.“We have a saying in the lab, which is: ‘Everything works in simulation’.&nbsp;You can always make your algorithm work in simulation, and then you get out in the field and things go wrong. So&nbsp;one thing we do in the lab is we always do the full stack. That’s why we need to have real robots.&nbsp;And we don’t only do experiments with real robots in the lab, we do them in the field.” <h3>MIST</h3>The lab he’s referring to is known as Polytechnique’s MIST, which stands for Making Innovative Space Technology. Dr. Beltrame is the director of the lab, which focuses on computer engineering targeted towards space technologies. In addition to the researchers, the lab is home to a *lot* of robots. There are big ones, small ones, wheeled ones, flying ones (drones) – literally “hundreds” of robots at the lab.But as Dr. Beltrame emphasised, proving that something will truly work requires testing in environments that are similar to what might be found on the moon or elsewhere. Locations where he’s carried out fieldwork include:<ul><li>Lava Beds National Monument in California (with <a href="https://www.jpl.nasa.gov/" target="_blank">NASA&nbsp;</a><a href="https://www.jpl.nasa.gov/" target="_blank">JPL</a>)</li><li>The Kentucky mega-cave with the <a href="https://costar.jpl.nasa.gov/" target="_blank">CoSTAR</a> team</li><li>Tequixtepec&nbsp;in Mexico with SpéléoQuébec</li></ul>Just check out the images below of field work, courtesy of Dr. Beltrame:<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2NTYyNDU0NzUyLUlNRy0yMDIzMDMyMy1XQTAwMDAuanBnLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="Tight Space Exploration">Tight Spaces<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2NTYyNDYyOTQyLWx1bmFyc2V0dGluZy1qcGcud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib">ESA Photo<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2NTYyNDg0ODU3LWltYWdlLXNjYWxlZC53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="Cave Drone">Cave Drone<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2NTYyNTAzNzAwLWRyb25ldGVhbS1qcGcud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="ESA Team">ESA Photo<h3 id="isPasted">The InDro Connection&nbsp;</h3>Some of the robots used in the MIST lab – and perhaps eventually on the moon – arrived via InDro Robotics, a North American distributor for AgileX. In fact, Polytechnique has purchased a number of AgileX products, including platforms that InDro has modified to help speed the R&amp;D process. These include:<ul><li>24 LIMOs and simulation table</li><li>AgileX Scout Mini</li><li>AgileX Scout 2.0</li><li>Two AgileX Bunker Mini platforms, with custom builds by InDro</li></ul>We’ve <a href="https://indrorobotics.ca/new-limo-pro-ros2-models-bring-advanced-abilities-to-rd/" target="_blank">written about the LIMO before</a> – a small, affordable and versatile robot capable of perceiving its environment and even Simultaneous Localization and Mapping out of the box. It’s also an ideal size, particuarly when doing multi-agent/swarm robotics, for use in the lab. (You’d run out of space pretty fast with something much larger).“The LIMOs are a very good platform for Simultaneous Localization and Mapping &nbsp;– and perception in general,” says Beltrame.He says they’re a good choice “because they have a 3D camera, they’re lighter, agile, and are sufficiently low in cost. So we can use them in large numbers.&nbsp;Another good thing about the LIMOs is that once you have a lot of similar robots that are reasonably agile, you can actually make a full deployment of software (across all robots).”That makes them an ideal platform for multi-agent research and development.“For example, we developed this tool called Swarm SLAM where many robots collaborate to have a better perception of the environment. We’re currently testing it with the full fleet of LIMOs. That’s&nbsp;something we would have believed impossible with larger robots for logistical reasons.”Though the focus is firmly on space, the Polytechnique Montréal research has applications on earth. Swarms of robots could aid in disaster response, Search &amp; Rescue, and more.<h3>&nbsp;</h3><h3>Favourite Robot</h3>The LIMO isn’t the only AgileX product in Polytechnique’s stable. And while Beltram likes all of them, he has a soft spot for one in particular.“I would say that my favorite robot is the <a href="https://indrorobotics.ca/scout-mini-2/" target="_blank">Scout Mini</a>,” he says. “It’s fast, it’s agile and the control is extremely precise.”In fact, Beltrame often takes the Scout Mini with him when doing school presentations. It’s small enough to be carried in the trunk of his car and hand-carried to classrooms. His team has also used the platform to test a new code for path planning and sophisticated energy calculations. It’s capable of tracking the additional energy required for climbing inclines, for example, then calculating when the robot needs to return home to wirelessly recharge.As always, InDro works with clients to deliver precisely what they need. This saves time for those institutions and corporations on builds, allowing them to get on with the business of R&amp;D.“We’ve done quite a bit of integration for them,” says <a href="mailto:lcorbeth@indrorobotics.com" target="_blank">Luke Corbeth</a>, InDro’s Head of R&amp;D Sales.“For example (see picture below), we provide a top plate with all required hardware mounted and integrated. They then add their own sensors, protective structure, etc. So this is a great example of how we work with clients on a case-by-case basis depending on their needs as robotics isn’t one-size-fits-all.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2NTYyODUzMDA0LWltYWdlLnBuZy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="Tracker Robots"> <h3 id="isPasted">One Small Step...</h3>With all of this research, what comes next? Will the work being done today at Polytechnique eventually find its way off this planet?“The answer is it’s going to happen very soon,” says Beltrame.&nbsp;Sometime later this year, a rocket will head toward the moon carrying three small robots. It’s called the Cadre mission.A trio of small rovers will work as a team to explore the moon autonomously, mapping the subsurface in 3D, collecting distributed measurements, and showing the potential of multirobot missions,” says <a href="https://www.jpl.nasa.gov/missions/cadre" target="_blank">NASA’s JPL website.&nbsp;</a>One of Beltrame’s students is working on that mission with JPL.“This is one example of how the work that we’ve been doing in this lab, in the end – through students that were here – become real missions,” says Beltrame.And that’s not all. As early as 2026, a Canadian-built rover could land on the moon in Canada’s first moon mission.Its task? To explore the moon’s south polar region in search of “water ice.” This ice is critical to long-term human habitation on the moon – and can also be converted to fuel, both for energy on the moon and potentially to refuel other spacecraft with destinations further afield.“I have an engineer from the Canadian Space Agency that’s a student of mine that’s developed the Mission Planner. So the idea is that we – our lab – developed the Mission Planner for the Canada rover that’s going to the moon.”Here’s a look at that planned mission, from the CSA:<iframe width="640" height="360" src="https://www.youtube.com/embed/Ti4a0oiWsMk??si=gdkI-fUB2n3fT89r&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&amp;lt;span class="fr-mk" style="display: none;"&amp;gt;&amp;amp;nbsp;&amp;lt;/span&amp;gt;&amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;gt;&amp;amp;amp;nbsp;&amp;amp;lt;/span&amp;amp;gt;&amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;lt;span class="fr-mk" style="display: none;"&amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;nbsp;&amp;amp;amp;amp;amp;lt;/span&amp;amp;amp;amp;amp;gt;</iframe><h3 id="isPasted">And There's More</h3>There was some big news this week from Polytechnique Montréal. On January 24 it announced the formation of ASTROLITH, a body for “research in space resource and infrastructure engineering.”It’s the first Canadian group dedicated to lunar engineering, according to a news release.“Comprising experts from all seven Polytechnique departments, ASTROLITH will pursue the mission of helping to develop next-generation technologies and training the engineers of tomorrow to ensure Canada’s presence in space and lunar exploration, as well as addressing critical needs on our planet within the context of climate change, resource management and sustainable development,” reads the release.So while the emphasis is on the moon, ASTROLITH will also result in some very practical – and urgent – use-cases on our home planet.“As encapsulated in its Latin motto Ad Lunam pro Terra, ASTROLITH is dedicated to developing technologies with direct impacts here on Earth: enabling development of infrastructure in the Far North or facilitating the energy transition, for example,” says the release.“Indeed, the research unit’s founding members are already involved in developing technologies in various areas related to space and extreme environments, from design of resilient habitats and infrastructures for remote regions to deployment of cislunar communications technologies to development of advanced robotics systems for prospecting and mining, among many others. Their work is bolstered by contributions from specialists in life-cycle analysis, sustainable development and space-related policy development.”The team is composed of academics and researchers that span all seven Polytechnique departments. Beltrame, not surprisingly, is on the team – which is pictured below. (He’s in the back row, centre.)<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1706563288421-Screenshot-2024-01-26-at-9.03.42-AM.png.webp" style="width: 300px;" class="fr-fic fr-dib" alt="Research Team"><h3 id="isPasted">INDRO’S TAKE</h3>We find the work being carried out at Polytechnique Montréal, the MIST lab – and now ASTROLITH – both fascinating and important. It’s also a terrific example of how dedicated researchers and students can develop and test projects in the lab that eventually have real-world (and off-world) applications.“I’m incredibly impressed with the work being carried out here, and the fact it can be put to positive use-cases both on earth and in space,” says InDro Robotics CEO Philip Reece.“We wish Dr. Beltrame and his colleagues well, and we’ll certainly be watching these lunar missions with great interest. It’s always a pleasure when InDro can support teams doing important work like this.”You can find more about the MIST lab <a href="http://mistlab.ca/" target="_blank">here</a>. And if you’d like to talk about AgileX robots (or any other robotic solution), connect with the InDro experts <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank" rel="noopener noreferrer">here</a>.&nbsp;

Bridging Earth and Lunar Exploration with Advanced Research

Robots on Earth Help Prepare for Research on the Moon

Humans, you might have noticed, aren’t perfect. We crash cars. We injure ourselves performing hazardous labor like mining and construction. And we fare poorly in extreme environments we’d like to explore – like the deep ocean or the vacuum of space.But <a href="https://profiles.stanford.edu/marco-pavone" data-extlink="" target="_blank">Marco Pavone</a>, associate professor of aeronautics and astronautics, and his students in the <a href="https://stanfordasl.github.io/" data-extlink="" target="_blank">Autonomous Systems Lab (ASL)</a> hope that emerging technologies like self-driving cars and space robots could one day help us get around these shortcomings.“It takes a lot of effort and equipment to keep people alive in space,” said <a href="https://stanfordasl.github.io/people/stephanie-newdick/" data-extlink="" target="_blank">Stephanie Newdick</a>, a PhD student in Pavone’s lab. And we’re not exactly indestructible on Earth, either. Sometimes, she said, “It just makes more sense to send a robot.”An ideal self-driving car, for instance, should make fewer mistakes than a human driver, allowing passengers to reach their destinations more efficiently and lowering the risk of an accident. And autonomous space robots could repair satellites or explore Martian landscapes – saving astronauts from life-threatening tasks while also pushing the boundaries of space exploration.<h2>An endless stream of decisions</h2>Achieving that level of competence for a space robot or self-driving car, however, is easier said than done. Autonomous systems like these must be able to make decisions and then translate them into physical actions in unpredictable – or completely alien – environments.The ASL tackles both issues, starting with designing decision-making algorithms that treat any scenario as a cost-benefit optimization problem. A self-driving car, for instance, must calculate which series of actions allow it to cross an intersection fastest while avoiding pedestrians and not jostling passengers – all in a few milliseconds. In addition to following fundamentals of good driving, such as staying within lines and abiding by traffic signals, this also involves anticipating the reactions of people in its environment.“If the robot does something, how might a nearby human respond?” Pavone said. “And based on that, how does the robot make its next decision?”Decision-making models also have to navigate the mishmash of cues a car might encounter – recognizing, for instance, that a stop sign printed on a billboard does not mean “stop.” Pavone’s lab is using large language models to design systems that can account for context in scenarios like this. Ideally, Pavone said, those systems will be able to learn from experience.“You see a lot of ‘firsts’ in your life, but you appeal to past experience and you don’t freak out,” Pavone said. “It’s pretty hard to do that with a robot.”<h2>New tools for new worlds</h2>All that decision-making does no good, however, unless a robot can translate it into effective physical actions in unfamiliar environments – for instance, zero gravity.That’s why Pavone’s lab has an air hockey table (or something close to one). Robot prototypes on its ultra-smooth granite surface shoot jets of air downwards and sideways, gliding over it like a puck. This way, Pavone’s lab can test how well the robots’ algorithms translate into lateral (though not vertical) motion in a zero-gravity-like environment.To ensure robots can do their jobs once they get where they’re going, the ASL also works on gripping tools – such as microspines for hooking into irregular surfaces and a <a href="https://news.stanford.edu/2021/05/20/gecko-gripper-tested-aboard-iss/" data-extlink="" target="_blank">gecko-inspired adhesive</a> co-developed with the <a href="http://bdml.stanford.edu/Profiles/MarkCutkosky" data-extlink="" target="_blank">Cutkosky lab</a>. Technologies like these could enable robots like Astrobee, a free-floating assistant aboard the ISS, to move cargo, fetch tools, make repairs, and clear debris.“An astronaut’s time is very valuable, but a significant amount of it is spent on housekeeping. Ideally, you would use a robot for that,” Pavone said, chuckling. “It’s fascinating work, but at the end of the day, we’re building robots to remove junk.”But the ASL’s designs could also help with space exploration. Take <a href="https://news.stanford.edu/2021/05/03/bringing-sci-fi-concepts-real-space-exploration/" data-extlink="" target="_blank">ReachBot</a>, one of Newdick’s main projects. Its eight arms extend like measuring tape with grippers at the ends. Paired with an autonomous navigation system, this could let it clamber around caves and tunnels on Mars without risking a human life to explore in person – but probably not anytime soon.“If I’m really lucky, in 20 years, my work will inspire another robot that does something different, but related,” Newdick said. “But if some part of what you do can contribute to something bigger, then that’s the goal.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1703984910214-20231129_ReachBot__95A0075.png" style="width: 300px;" class="fr-fic fr-dib">Schematic of the newer generation of ReachBot, showing some of the technology and features it could have to help with exploration of other planets and celestial bodies. | Image courtesy of Autonomous Systems Lab<h2 id="isPasted">Always more to explore</h2>Deploying autonomous systems has its downsides – especially on Earth, where robots could potentially take some jobs currently held by humans.“That’s always in the back of my mind,” Pavone said. “We develop systems that hopefully will create opportunities in the long run, but in the short term might cause significant pain for some segments of society.”Pavone and his students collaborate with other schools across Stanford to understand those consequences and design different types of strategies to mitigate them – such as <a href="https://hai.stanford.edu/news/equitable-approach-reducing-traffic-through-congestion-pricing" data-extlink="" target="_blank">using AI to redistribute traffic toll money</a> from drivers who can afford tolls to those who can’t.But with space robots, not even the sky’s the limit. Pavone gives the example of Mars: 20 years ago, the goal was just to land a robot there. Now, we’ve sent helicopters to take aerial views of the surface, and with ReachBot, we’re hoping to look underground.“You always want to get into more complicated environments,” Pavone said. Newdick agreed: Given the resources, she said, she’d love to design a robot to crawl down the vents in the icy crust of Enceladus, a moon of Saturn, to search for signs of life – another exploration likely impossible without autonomous robots.“Learning where we came from, where the planets and stars came from – that’s always been a curiosity,” Newdick said. “We can learn a lot doing science on Earth, but we can learn more if we also look elsewhere.”

Associate Professor Marco Pavone and PhD student Stephanie Newdick holding their ReachBot prototype. | Photo courtesy of Andrew Brodhead

From space robots to self-driving cars, Stanford’s Autonomous Systems Lab looks to push the boundaries of exploration and boost the safety and efficiency of everyday tasks.

The limitations of being human got you down? There's a robot for that

Example output of DeepScenario’s AI software that detects and tracks all moving objects in a scene in a fully automated way. (DeepScenario) 

There are many divergent-but-related ideas about how best to leverage simulation to ‘teach’ and test automated vehicles. Global AV simulation experts continue to attack the technology’s persistent obstacles.

AV simulation testing faces a long and winding road

In this episode, we discuss how active noise canceling technology works, what makes it so complex yet so useful, and why it is the unsung and underappreciated hero in your car.

Podcast: How To Make a Honda As Quiet As a Rolls Royce

The switch to e-mobility is also taking shape in Austrian cab fleets. This year, ten cab ranks and 66 e-vehicles will be retrofitted with "Matrix Charging" in Vienna and Graz as part of the "eTaxi Austria" project. The automated conductive charging technology was developed by the high-tech company Easelink. For the globally unique pilot project, FACTUREE is supplying various turned and milled parts made of aluminum. They are installed in the integrated "Matrix Charging Connector". During the charging process of an eTaxi, the connector is automatically lowered from the vehicle underbody and connected to a charging pad at the parking lot for charging.&nbsp;According to the national energy and climate plan, only zero-emission cabs and rental cars will be allowed in Austria from 2025. This measure paves the way for e-mobility. The stated goal is to reduce emissions and improve the quality of life in cities. However, previous eTaxi initiatives have shown that trips to charging stations as well as on-site charging times lead to uneconomic waiting times. The eTaxi Austria project aims to remedy this situation and relies on automated charging directly at charging stations. This avoids interruptions to get to the next charging station and makes the economic operation of electric cabs possible.&nbsp;<h2>Automated charging, a precursor to autonomous driving</h2>The eTaxi Austria project supports the switch of Vienna and Graz cab fleets to e-mobility primarily by providing charging options at the cab stand. Among others, the high-tech company Easelink, headquartered in Graz, is involved in the ambitious project. Over 60 eTaxis in the Vienna and Graz area will be equipped with the automated charging technology "Matrix Charging" developed by Easelink. Users of an e-car are relieved of having to plug in and unplug the vehicle thanks to the special technology. Easelink's goal is to standardize automated charging and thus prepare the e-fueling process for autonomous driving as well.&nbsp;The Matrix Charging System consists of two main components. Its compact design allows the Matrix Charging Connector to be easily integrated into the underbody of an electric vehicle. The vehicle unit is joined by an infrastructure unit directly at the parking space, the Matrix Charging Pad. As soon as the e-vehicle parks above the Charging Pad at the parking lot, the Connector is automatically lowered by the e-vehicle and physically connected to the Charging Pad at the cab stand. The charging process is automated without the driver having to leave the e-vehicle.&nbsp;<h2>FACTUREE supplies important components for the core element of the charging system </h2>FACTUREE supplies important components for the core element of the charging system For one of the two core components of the conductive charging system, the Matrix Charging Connector, the online manufacturer FACTUREE supplies various small turned and milled parts made of aluminum. Easelink benefits from great flexibility and plannability through the cooperation. This is because FACTUREE draws on a wide range of network partners, manufacturing techniques and surface treatments. As a result, the online manufacturer can constantly offer a very wide range of materials and has virtually unlimited production capacities - this is a major competitive advantage in times when supply problems are more frequent for classic contract manufacturers due to global crises.The Matrix Charging System can already be retrofitted to the most common electric vehicles on the market. The eTaxi Austria project is preparing the rollout of the automated charging infrastructure, thus setting the course for the mobility turnaround.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1702283550100-Matrix-Charging-System-with-Text_%C2%A9-Easelink_1200x800-768x512.jpg" style="width: 300px;" class="fr-fic fr-dib"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1702283555195-Matrix-System-Wireframe-Vehicle_%C2%A9Easelink_1200x675-768x432.jpg" style="width: 300px;" class="fr-fic fr-dib">

The switch to e-mobility is also taking shape in Austrian cab fleets. This year, ten cab ranks and 66 e-vehicles will be retrofitted with "Matrix Charging" in Vienna and Graz as part of the "eTaxi Austria" project. 

Climate-friendly mobility is gaining momentum: FACTUREE is on board in Austrian eTaxis

<h2 dir="ltr" id="isPasted">FREE REPORT:&nbsp;Reducing Human Driver Error and Setting Realistic Expectations with Advanced Driver Assistance Systems</h2>Thousands die or are injured each year in automobile crashes. Bringing automated driving systems technologies into the advanced driver assist systems (ADAS) and connected vehicle space will help humans drive more safely and better prepare us for automated vehicles (AVs).Learn more about ADAS and ADS implementation with the goal of reaching zero deaths and serious injuries by downloading this FREE report from SAE International (valued at $50).&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyMzIxNDMwMTE3LXNhZSBmcmVlIHJlcG9ydC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><iframe src="https://c8056756.sibforms.com/serve/MUIFAIm7QnvQQ9ZyzkX--H5jNS-TSb9GWr5SQBXi_SUjRPpt67CBveq_yevZYMSeiJ-O1dwYw05f2kl-Q7sfNDRem1Y-03GXYZV4qkTbJeR_K_VpJzgMk_8Ry8fjyfgJ7fAMrCmcRHc1KXsSTuhIU5p5pBa3F1-TGDgOdQgcF-pU8cSTrl8JF2j2b9gl6-5e66Ro-rltcIrGxsRG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 740px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe><hr><h2>Provizio promises its 5D Perception stack can safely compete with expensive lidar sensors at a fraction of the cost.</h2>"Safety first” is more than a catchphrase. For sensing company Provizio, it’s the only way the transportation industry should introduce autonomous vehicles. In Provizio’s view, using AV building blocks – technology such as automatic emergency braking and lane-keep assist – can be valuable in ADAS systems, but they should not be used to drive vehicles until the perception problem has been solved.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyMjgyNDM3MTM3LXByb3ZpemlvLXJhZGFyLXN1cGVyLXJlc29sdXRpb24tZ2FsbGVyeS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">An example image using data taken from Provizio’s “super-resolution” radar sensor.<blockquote>“It’s not that we’re skeptical about autonomous driving, it’s just that we strongly believe that the industry has taken this wrong path,” Dane Mitrev, machine learning engineer at Provizio, told SAE at September 2023’s AutoSens Brussels conference.</blockquote>“The industry has looked at things the other way around. They tried to solve autonomy first, without looking at accident prevention and simpler ADAS systems. We are building a perception technology which will first eliminate road fatalities, then deliver next-generation ADAS – and then solve autonomy. We believe that’s the right order.”Obviously, the industry is in favor of AVs. A group of 11 mobility and automotive companies released a 157-page paper in 2019 called “Safety First for Automated Driving.” Those companies — Aptiv, Audi, Baidu, BMW, Continental, Daimler, FCA US LLC, HERE, Infineon, Intel and Volkswagen — used the paper to propose a non-binding organized framework focused on “the development, testing and validation of safe automated passenger vehicles,” but did not attempt to establish any actual limits on what companies could do as they developed AVs. In the years since, AV technology has continued to advance, but not with the mission to solve sensing and important safety issues before teaching the vehicles how to maneuver.Provizio’s solution is its 5D Perception driving platform, named for the way it uses five different inputs to sense the environment. There’s the 3D radar information of an object in space, plus velocity information, plus the fifth dimension: perception, which means AI embedded in the hardware. Mitrev said the final vision of this technology will be powered by radar and cameras, not light detection and ranging (lidar). Radar’s benefits are well known: the sensors are smaller (and thus simpler to integrate) and less expensive than lidar and can see in all weather conditions. For now, though, Provizio is working with lidar, especially around its hometown.<h3>Irish sensors are smiling</h3>Provizio is headquartered in Limerick, Ireland, and has offices in North Ireland and Pittsburgh, Pennsylvania. In late 2022, Provizio signed a deal with micromobility company Voi to test 5D perception on Voi’s scooters. Alongside potential OEM and Tier 1 partnerships he couldn’t discuss, Mitrev said Provizio also has a partnership with Jaguar Land Rover, supporting tests at a future mobility campus in Ireland.These real-world tests involve collecting data from lidar to train the new radar AI. Provizio synchronizes the lidar and radar sensors, then extracts value from the lidar data at training time, embedding it into the neural net and teaching the neural networks based on these data streams. Imagine lidar as an old professor teaching a young radar sensor a few things, but sooner or later, the student – Provizio’s radar – won’t need the teacher anymore.<blockquote>“We lean fully towards a lidar-less path,” he said. “Lidars should not be involved, even in [SAE Level 5] vehicles. They’re not necessary.”</blockquote>The same is not true for cameras. To understand semantic information such as traffic signs, Provizio’s system uses cameras to augment the radar data.“Provizio is a perception company, first of all,” Mitrev said. “We are not a radar company. We’re not a car manufacturer. We’re a perception company, we have this radar and we have a perception platform that fuses radars and cameras and provides this low-cost solution.”Provizio was founded by Barry Lunn following the acquisition of his company Arralis, which focused on millimeter-wave communication technology for satellite communications and aerospace markets. Provizio’s radar technology grew out of radar work Lunn was familiar with in the aerospace industry. Instead of a commercial, off-the-shelf radar unit, Provizio is “redefining radars,” Mitrev said, by adding AI neural networks to the radar GPUs so all critical decisions can be made on the radar itself with less than five milliseconds latency to make those decisions. He said Provizio’s goal for its 5D radar is to see up to 600 m (1,969 feet) in 360 degrees in all weather conditions.<blockquote>“Once we have this 5D perception, there is literally no reason to build any other system that will do ADAS or autonomy,” he said, adding that accident prevention is “something tangible that we can solve in the next one, three-to-five, years.”</blockquote>At CES 2023, Provizio said it planned to deliver the first of its 5D Perception platforms by 2025. Provizio’s commercial strategy relies on licensing its technology to Tier 1 suppliers and OEMs. Once better sensors are in more vehicles that accurately understand the world around them, that’s when we can start letting vehicles drive themselves, Mitrev said.“The end goal is mass adoption, to have our 5D perception on all vehicles or a lot of vehicles worldwide, then we bring down the road deaths,” he said. “We believe that this is the right path. The industry has taken this the other way around. [Starting by building a self-driving car] is just too complex, let alone with only cameras, as some companies claim.”

Provizio offered demo drives of its technology on the road in Las Vegas during CES in January 2023. These images show some of what the sensors saw during the drive, along with some of Provizio’s identifying data. (Provizio)

"Safety first” is more than a catchphrase. For sensing company Provizio, it’s the only way the transportation industry should introduce autonomous vehicles.

Autonomus safety with radar, not LiDAR

Thousands die or are injured each year in automobile crashes. Bringing automated driving systems technologies into the advanced driver assist systems (ADAS) and connected vehicle space will help humans drive more safely and better prepare us for automated vehicles (AVs).The Reducing Human Driver Error and Setting Realistic Expectations with Advanced Driver Assistance Systems report demonstrates in depth that ADAS can work.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxOTYxMzUxNTQ5LVNBRSBHVUlERS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"> <iframe src="https://c8056756.sibforms.com/serve/MUIFAMdP0A_OGmbm1FBUdHG4Kch9Yvcfk0o7_Lq0Ywvq_bW5DACvNKJTxu581PWdSkLw1Lzm_3zXkKgpDQ0qa9wZF0aMleCF9Dht6uXrWjeRGuMlIbLAG00tAE1k1Zj9J3qZyOnF08P7PZYck76ldPDyWzCbrD6Tvrokkm59XTxP7P4hV7Gz2fXT6er4g7NK0E8nq6obPbX1dAqy" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 740px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Get your copy of this report now.&nbsp;Free for Wevolver readers in December (usually $50).&nbsp;The path towards zero road deaths requires combining ADAS and ADS implementation with infrastructure engineering, law enforcement, education, emergency response, and public policy.<h2 dir="ltr">Unveiling the Potential of ADAS in Modern Transportation</h2>Together with SAE International we are offering our community free access to this report, a part of SAE International’s EDGE Research Research portfolio.&nbsp;The report delves into the heart of ADAS technologies, exploring their role in reducing human driver errors, which have been a longstanding challenge in the realm of road safety. With a focus on cutting-edge research and real-world applications, the report explores &nbsp;the critical ways in which ADAS is transforming everyday driving experiences, making roads safer for drivers, passengers, and pedestrians alike.Compiled by leading experts in the field, the report offers a comprehensive overview of the latest advancements and future prospects of ADAS. From autonomous braking systems to adaptive cruise control and lane-keeping assist, it covers a wide range of technologies that are integral to the modern driving landscape. This report is an essential resource for industry professionals and technology enthusiasts who are keen to stay ahead in the rapidly evolving world of automotive technology.<h2 dir="ltr">A Step Towards a Zero-Fatality Future</h2>SAE’s vision aligns with the global goal of achieving zero deaths and serious injuries on the road. By harnessing the power of ADAS, we are not only looking at a future where driving is more comfortable and convenient but also significantly safer. This report serves as a guide to understanding how close we are to realizing this vision and the steps needed to get there.Download Your Copy Today for Free!<iframe src="https://c8056756.sibforms.com/serve/MUIFAMdP0A_OGmbm1FBUdHG4Kch9Yvcfk0o7_Lq0Ywvq_bW5DACvNKJTxu581PWdSkLw1Lzm_3zXkKgpDQ0qa9wZF0aMleCF9Dht6uXrWjeRGuMlIbLAG00tAE1k1Zj9J3qZyOnF08P7PZYck76ldPDyWzCbrD6Tvrokkm59XTxP7P4hV7Gz2fXT6er4g7NK0E8nq6obPbX1dAqy" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 740px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>Ready to explore the world of ADAS and its impact on road safety? Download your copy of "Reducing Human Driver Errors with ADAS" today. Simply fill out the form on this page, and you'll gain immediate access to this essential resource. Join us in driving towards a safer, more efficient future.Read more content from SAE International on Wevolver <a href="https://www.wevolver.com/profile/sae.international" target="_blank">here.&nbsp;</a>

Download the limited edition free report now. 

New Report: The Technology Behind ADAS and a Crash-Free Future

Testing of a new automated shuttle is underway at the University of Michigan’s Mcity Test Facility to ensure the technology is ready to safely serve older adults and people with disabilities in the city of Detroit.Earlier this month, U-M researchers kicked off several months of testing with an automated vehicle provided by <a href="https://maymobility.com/" target="_blank" rel="noreferrer noopener">May Mobility</a>, a leader in the development and deployment of AV technology.&nbsp;The vehicle under test is identical to those destined for Detroit. The evaluation is taking place at the <a href="https://mcity.umich.edu/" target="_blank" rel="noreferrer noopener">Mcity Test Facility</a>—the world’s first purpose-built environment for testing connected and automated vehicles and technologies under controlled, realistic conditions. The shuttle will undergo a two-part test developed by Mcity, called the Mcity Safety Assessment Program.The protocol includes:<ul><li>A “Driver’s License Test” that serves a purpose similar to a human driver’s test, measuring basic competency in ordinary scenarios.&nbsp;</li><li>A “Driving Intelligence Test” that challenges AV software with a diverse set of dangerous driving scenarios—representing those that most often result in crashes, injuries and fatalities.</li></ul>The augmented reality program combines physical vehicles, infrastructure and obstacles with computer-simulated people, vehicles and other objects.“Automated vehicles have the potential to make transportation safer and cleaner while ensuring equitable access to mobility options—but only if consumers can trust the vehicles are safe,” said Mcity Director Henry Liu, who is also the Bruce D. Greenshields Collegiate Professor of Engineering and director of the Center for Connected and Automated Transportation.“We believe the Mcity Safety Assessment Program could serve as the blueprint for a publicly visible safety framework, helping to bring automated vehicle technology to market in a manner that truly benefits society.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701141819329-AVTesting-content2.jpg" style="width: 766px;" class="fr-fic fr-dib">The Driverless May Mobility vehicle stops automatically to avoid colliding with a child-size test dummy in the road. Photo: Marcin Szczepanski/Michigan EngineeringTesting of the shuttle for Detroit comes as recent traffic crashes related to driverless vehicles have raised broad concerns about safety.&nbsp;To prioritize safety, the city of Detroit’s Automated Driving Systems shuttle pilot will undergo a thorough evaluation process, beginning with the testing at Mcity, and the deployment of the shuttles will feature a skilled safety operator on board in each May Mobility vehicle. These operators will be available to answer questions and assist wheelchair users when boarding and exiting the shuttle.“From design to launch and beyond, we have embedded strong safety practices into all of our processes,” said Satvir Singh, director of product safety at May Mobility.&nbsp;“Participating in Mcity’s Safety Assessment Program is one crucial step to ensuring that each AV we deploy will bring our riders to their destinations safely and comfortably. With the help of our partners at Mcity, we feel confident that our vehicles will exceed expectations and set a new standard for AV technology safety in Detroit and across the world.”Testing of the shuttle at Mcity will likely run through February. From there, it will be sent to the <a href="https://acmwillowrun.org/" target="_blank">American Center for Mobility</a> in Ypsilanti, for extended high-speed testing.&nbsp;Designed with equity in mind, the pilot aims to provide Detroit residents aged 65 and older or those living with disabilities with free transportation. It is intended to facilitate seamless travel to and from essential destinations such as stores, medical appointments, workplaces and more.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701141849330-AVTesting-content1.jpg" style="width: 766px;" class="fr-fic fr-dib">U-M researchers Xintao Yan (center) and Linxuan He (left) adjust the safety testing technology while Tinghan Wang looks on from the challenge vehicle at Mcity. Photo: Marcin Szczepanski/Michigan EngineeringThe Detroit City Council unanimously voted to approve a $2.4 million contract between the Office of Mobility Innovation and May Mobility in July, and the city plans to deploy the shuttle in June 2024.&nbsp;“We are excited that the rigorous self-driving shuttle testing is underway with Mcity,” said Tony Geara, deputy chief of the city of Detroit’s Office of Mobility Innovation. “This thorough evaluation process underscores the city of Detroit’s commitment that self-driving technology will be safe and effective before it is on our streets.&nbsp;“We are starting this ambitious pilot project with the Mcity Safety Assessment Program as a critical step toward learning how self-driving vehicles can effectively serve real community needs in Detroit.”May Mobility is a U-M startup that was launched in 2017 with the help of Innovation Partnerships.

The University of Michigan, May Mobility and the City of Detroit look to boost public trust in self-driving vehicle technology.

Automated shuttle planned for Detroit starts safety testing at Mcity

Autonomous Vehicles (AVs) are poised to revolutionize human transportation by making it more safe, comfortable, and reliable. From passenger travel to cargo transportation, AVs equipped with AI and sensors offer tremendous potential. 

What is an Autonomous Vehicle: A Comprehensive Guide to its Engineering Principles and Applications

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Introduction

Sensing Technologies

Thinking and Learning

EDGE and RTOS

Communication and Connectivity

Security

Tech Stack

Examining the latest developments in self-driving vehicles.

Autonomous Vehicle Technology Report

autonomous vehicles

Enhancing Efficiency with AGVs and AMR

AGV vs AMR: A Comprehensive Comparison in the World of Automation

Chinese food delivery commercial Meituan testing an autonomous vehicle

The automotive industry undergoes a profound transformation through the IoT's power. Vehicle connectivity and autonomous driving redefine transportation, offering innovative opportunities. Embracing IoT's potential, our daily lives are poised for a significant shift in the automotive sector.

Driving Innovation: Harnessing IoT Potential in the Automotive Sector

In this article, we will dive deep into the world of simultaneous localization and mapping using Lidar technology. Lidar SLAM has been gaining popularity in recent years, thanks to its versatility and applications across various domains, including autonomous vehicles, mobile robotics, and indoor mapping.

Lidar SLAM: The Ultimate Guide to Simultaneous Localization and Mapping

Explore the key differences, advantages, and applications of lidar and radar technologies in modern engineering projects.

Lidar vs. Radar: Comprehensive Comparison and Analysis

Cellular Vehicle-to-Everything (C-V2X) technology is transforming the transportation industry by enabling advanced communication between vehicles, infrastructure, and other road users. This breakthrough technology has the potential to improve road safety, reduce traffic congestion, and support the development of autonomous vehicles.

Designing C-V2X Communication Systems: Key Engineering Considerations and Best Practices

Autonomous Vehicles

Autonomous Vehicle Technology Report

Introduction

1. Sensing Technologies

2. Thinking and Learning

3. EDGE and RTOS

4. Communication and Connectivity

5. Security

6. Tech Stack

What is an Autonomous Vehicle: A Comprehensive Guide to its Engineering Principles and Applications

Profiles