<html><head></head><body>Researchers at the Technical University of Munich (TUM) have developed a wheeled robot that makes its way through a crowd of people safely and without hesitation. A computer on board predicts the movement of people in the vicinity and how they are likely to react to the robot. From this, it calculates the fastest route. Similar algorithms could also be used for humanoid robots or autonomous driving to enable safe interaction between robots and humans.The little robot weaves its way through crowds of people on its wheels like a human. To make this possible, researchers from TUM Professor Angela Schoellig's Learning Systems and Robotics Lab have combined computing power, sensors and mathematical skills. ‘Our robot models the way people will react to its movements to plan its paths. This is the big difference to other approaches that typically ignore this interaction,’ explains Prof. Schoellig.<h2>New route calculated ten times per second</h2>A lidar system constantly sends laser beams into the surroundings, measures their reflections, and uses them to build a precise 360-degree map of what the robot sees. A special focus is placed on people walking around nearby. At the same time, sensors in the wheels measure the robot's own speed and the distances traveled. A computer processes this information, calculates the estimated distances people will cover in the next two seconds, and simultaneously plans the optimum route to the destination. ‘Our robot adjusts its route ten times a second while simultaneously recognizing people's paths,’ explains TUM researcher Sepehr Samavi.<h2>System learns from data based on people in crowds</h2>The robot, which Sepehr Samavi has named ‘Jack’, learns behavioral patterns from humans so that it does not constantly stop due to the risk of collisions. ‘Our mathematical model, on which the planning algorithm is based, was derived from human movements and translated into equations,’ explains Professor Schoellig. For Jack's decision, this means that he does not stop immediately as soon as a person approaches him. He takes into account that people will adapt to the situation, react and change their route slightly so that they do not collide with him. If someone remains on a collision course contrary to expectations, the robot changes its plans quickly and takes a different route - but does not stop.&nbsp;The researchers also incorporate data sets that show people's behavior in crowds. The robot, which has already been used outside of the laboratory, is constantly learning and becoming more human-like: ‘Jack knows his destination, observes people and sees where they are going to constantly optimize his own paths,’ says Prof. Schoellig, ‘almost like a human being.’&nbsp;The TUM researchers have already reached the third evolutionary stage with the new algorithm. Instead of ‘only’ reacting to a situation (stage 1) or ‘merely’ predicting the movements of oncoming people (stage 2), the TUM robot is interactive (stage 3). ‘On the one hand, it predicts other people's movements, but it also manages to influence these people through its behavior and simultaneously avoid collisions,’ explains researcher Sepehr Samavi. &nbsp;&nbsp;<h2>Development for use in autonomous driving</h2>It is precisely such interactive scenarios that form the bottleneck in autonomous driving, says Prof Schoellig. For example, if a vehicle starts accelerating on a highway on-ramp, many drivers will change lanes or tap the brakes. The new approach makes it possible to consider the reaction of others in such a scenario. However, the researchers are initially looking at applications in delivery robots or with wheelchair users. The advantage: these vehicles can reach their destination independently and reliably. Even humanoid robots could benefit from the new algorithms. However, the intelligent vehicle has one decisive disadvantage: ‘A moving robot can simply stop if necessary - humanoids are still quite wobbly and quickly lose their balance,’ says Prof. Schoellig.<hr>PublicationsSepehr Samavi, James R. Han, Florian Shkurti, and Angela P. Schoellig; SICNav: Safe and Interactive Crowd Navigation Using Model Predictive Control and Bilevel Optimization; IEEE Transaction on Robotics; 2025; <a href="https://ieeexplore.ieee.org/document/10726864" target="_blank" rel="noreferrer">https://ieeexplore.ieee.org/document/10726864</a><hr>Further information and links- Prof. Angela Schoellig heads the Chair of Safety, Performance, and Reliability for Learning Systems at the TUM School of Computation, Information and Technology. She is responsible for international relations on the board of the Munich Institute of Robotics and Machine Intelligence (MIRMI) and also coordinates the Robotics Institute Germany, which is funded by the Federal Ministry of Education and Research (BMBF) – <a href="https://www.robotics-institute-germany.de/" target="_blank" rel="noreferrer">https://www.robotics-institute-germany.de/</a>. She is also a member of the Munich Data Science Institute (MDSI) at TUM. &nbsp;&nbsp;- TUM researcher Sepehr Samavi introduces to Jack: <a href="https://youtu.be/7pxZhRulXVY" target="_blank" rel="noreferrer">https://youtu.be/7pxZhRulXVY</a>- Scientific video: <a href="https://www.youtube.com/watch?v=BY23UXwM1sM" target="_blank" rel="noreferrer">https://www.youtube.com/watch?v=BY23UXwM1sM</a></body></html>

Sepehr Samavi and Prof Angela Schoellig next to robot Jack. Credit: Astrid Eckert / TUM

Researchers at the Technical University of Munich (TUM) have developed a wheeled robot that makes its way through a crowd of people safely and without hesitation. A computer on board predicts the movement of people in the vicinity and how they are likely to react to the robot.

Robot Jack moves like a human

<html><head></head><body>VOSTOK, a local navigation system for autonomous systems, has been unveiled at ITMO. Useful in areas with poor or non-existent GPS signal, the technology can be applied in autonomous vehicles, unmanned aircraft employed on icebreakers along the Northern Sea Route, or drones delivering groceries to the countryside. Compared to its counterparts, the system can gauge its position and speed faster and with more precision: the coordinates are calculated with an accuracy of up to four millimeters and speed – with an accuracy of up to two centimeters per second.When navigating and planning routes, unmanned systems and vehicles rely on GPS technology. Unfortunately, it can often be unreliable in dense urban settings or even be entirely unavailable in closed spaces, such as rooms or tunnels. Even a two-meter difference between the actual and the calculated location can prevent a small drone from landing precisely on a charging base or placing a package in the correct spot.Local navigation can remedy these complications. For instance, the vehicle can use its onboard camera to scan QR codes placed along the landing area. However, this approach, too, has its drawbacks: it requires proper lighting and a great number of QR codes of various sizes that could be detected from different distances. Another local navigation approach involves radio beacons and ultrasound; with its receivers, the drone detects sources of radio waves or sound and thus locates itself. However, radio waves are reflected from walls, while ultrasound is drowned out by the sound of the drone’s blades, which hinders calculation of coordinates.Scientists from ITMO’s <a href="https://en.itmo.ru/en/faculty/105/fakultet_bezopasnosti_informacionnyh_tehnologiy.htm" target="_blank">Faculty of Secure Information Technologies</a> have developed VOSTOK, a local navigation system that helps drones navigate, land, and take off in areas without GPS access. Unlike its analogs, VOSTOK doesn’t require a camera, radar, or lidar to navigate and the system can be assembled from cheap, easily accessible components. Additionally, the new solution is faster and more accurate: coordinates are calculated with an accuracy of up to four millimeters and at several thousand times per second, surpassing real-time kinematic positioning (GPS RTK), while speed is tracked ten times per second with an accuracy of up to two centimeters per second – this is the same level of accuracy as with GPS. Thanks to these parameters, a drone can land on or launch from a moving vehicle even in dense fog.<blockquote>“Our method is based on the classic principles of radio navigation and radio direction finding (RDF) and works not unlike talking to two people standing in one place and communicating with a megaphone. If you want to hear one of them, you have to face their megaphone; and if you want to hear both – you need to put yourself between the two. In VOSTOK, two LEDs placed at an angle to each other play the role of the megaphones. Each module has two pairs of LEDs. They shine on the drone’s light detectors with a signal modulated to different frequencies. The light detectors receive the signal while a special module amplifies and processes it, calculating the drone’s coordinates and sharing the data with the onboard computer. In real time, we can see the drone’s exact location, as well as any changes in its altitude or angle of tilt,” explains&nbsp;Timofey Melnikov, the head of the project and a first-year PhD student at ITMO’s Faculty of Secure Information Technologies.</blockquote><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741847269514-1419400+%281%29.jpg" style="width: 100%px;" class="fr-fic fr-dib">The beacon forming the spacial navigational field with the help of two pairs of LEDs. Two pairs of such modules create a 3D navigational field. Photo by Dmitry Grigoryev / ITMO.NEWSVOSTOK can have various applications. For instance, if used along the Northern Sea Route, the system can ensure automatic landing of unmanned helicopters onto moving icebreakers. It can also automate the routes of driverless trucks in quarries, where dust is a problem for navigation, as well as drone deliveries of groceries to the countryside.Currently, the signal from two pairs of modules covers an 8x8 meter room with ceilings up to 3 meters, but the system can easily be scaled up by increasing the number of modules, their power, and configuration.&nbsp;In the future, the developers are planning to test the system inside a sports hall and configure the system to allow vehicles to position themselves relative to one another, and not just within a fixed space. They hope that their solution will become a part of a robodrone arena, a playground for developers to collect data and polish their drone control algorithms.<blockquote>“Using VOSTOK, we are planning to create a 3D autopilot that will be able to fly a drone as well as a human pilot. Typically, to create such an autopilot, you would need to record several hours of flight, process camera data, and use it to train an AI model. With our system, such a dataset can be collected based on coordinates received by the drone in-flight. Thus, the AI model will be more precise, as we collect more data per second than other systems, while also being significantly cheaper because you don’t need a motion capture system. With a 3D autopilot, it will be possible to reduce the time required for routine tasks, such as monitoring agricultural land or assessing bridges, tunnels, or similar structures, as the drone will require less time to turn or avoid obstacles at a higher speed,” explained&nbsp;Andrey Boyko, the project’s research consultant and a senior researcher at the Faculty of Secure Information Technologies.</blockquote><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741847295587-32_1+%281%29.gif" style="width: 100%px;" class="fr-fic fr-dib">Calculation system demonstration. Andrey Boyko is holding a mobile module with three photo receivers. Data from the module is passed to the computer. On the screen on the left, you can see received raw signal amplitudes, while on the right the three spiral shapes that correspond to the movement of the three receivers on the mobile module. Video by Dmitry Grigoryev / ITMO.NEWS</body></html>

The mobile model on a quadcopter for in-flight data collection. Photo by Dmitry Grigoryev / ITMO.NEWS

VOSTOK, a local navigation system for autonomous systems, has been unveiled at ITMO. Useful in areas with poor or non-existent GPS signal, the technology can be applied in autonomous vehicles, unmanned aircraft employed on icebreakers along the Northern Sea Route, or drones delivering groceries.

Researchers Develop Analog GPS System for Autonomous Systems

Innovative semiconductor solutions are essential for addressing ADAS sensor integration challenges, including power management, communication reliability, and safety enhancements, enabling smarter and safer automotive systems.

Overcoming ADAS Sensor Challenges with Nexperia: Insights from Nandor Bodo

In the future, autonomous drones could be used to shuttle inventory between large warehouses. A drone might fly into a semi-dark structure the size of several football fields, zipping along hundreds of identical aisles before docking at the precise spot where its shipment is needed.Most of today’s drones would likely struggle to complete this task, since drones typically navigate outdoors using GPS, which doesn’t work in indoor environments. For indoor navigation, some drones employ computer vision or lidar, but both techniques are unreliable in dark environments or rooms with plain walls or repetitive features.MIT researchers have introduced a new approach that enables a drone to self-localize, or determine its position, in indoor, dark, and low-visibility environments. Self-localization is a key step in autonomous navigation.The researchers developed a system called <a href="https://www.mit.edu/~fadel/papers/mmDrone-infocom2025.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">MiFly</a>, in which a drone uses radio frequency (RF) waves, reflected by a single tag placed in its environment, to autonomously self-localize.<iframe width="640" height="360" src="https://www.youtube.com/embed/TyySRwsUpbY?&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"></iframe>Because MiFly enables self-localization with only one small tag, which could be affixed to a wall like a sticker, it would be cheaper and easier to implement than systems that require multiple tags. In addition, since the MiFly tag reflects signals sent by the drone, rather than generating its own signal, it can be operated with very low power.Two off-the-shelf radars mounted on the drone enable it to localize in relation to the tag. Those measurements are fused with data from the drone’s onboard computer, which enables it to estimate its trajectory.The researchers conducted hundreds of flight experiments with real drones in indoor environments, and found that MiFly consistently localized the drone to within fewer than 7 centimeters.“As our understanding of perception and computing improves, we often forget about signals that are beyond the visible spectrum. Here, we’ve looked beyond GPS and computer vision to millimeter waves, and by doing so, we’ve opened up new capabilities for drones in indoor environments that were not possible before,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of a&nbsp;<a href="https://www.mit.edu/~fadel/papers/mmDrone-infocom2025.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">paper on MiFly</a>.Adib is joined on the paper by co-lead authors and research assistants Maisy Lam and Laura Dodds; Aline Eid, a former postdoc who is now an assistant professor at the University of Michigan; and Jimmy Hester, CTO and co-founder of Atheraxon, Inc. The research will be presented at the IEEE Conference on Computer Communications.<h2>Backscattered signals</h2>To enable drones to self-localize within dark, indoor environments, the researchers decided to utilize millimeter wave signals. Millimeter waves, which are commonly used in modern radars and 5G communication systems, work in the dark and can travel through everyday materials like cardboard, plastic, and interior walls.They set out to create a system that could work with just one tag, so it would be cheaper and easier to implement in commercial environments. To ensure the device remained low power, they designed a backscatter tag that reflects millimeter wave signals sent by a drone’s onboard radar. The drone uses those reflections to self-localize.But the drone’s radar would receive signals reflected from all over the environment, not just the tag. The researchers surmounted this challenge by employing a technique called modulation. They configured the tag to add a small frequency to the signal it scatters back to the drone.“Now, the reflections from the surrounding environment come back at one frequency, but the reflections from the tag come back at a different frequency. This allows us to separate the responses and just look at the response from the tag,” Dodds says.However, with just one tag and one radar, the researchers could only calculate distance measurements. They needed multiple signals to compute the drone’s location.Rather than using more tags, they added a second radar to the drone, mounting one horizontally and one vertically. The horizontal radar has a horizontal polarization, which means it sends signals horizontally, while the vertical radar would have a vertical polarization.They incorporated polarization into the tag’s antennas so it could isolate the separate signals sent by each radar.“Polarized sunglasses receive a certain polarization of light and block out other polarizations. We applied the same concept to millimeter waves,” Lam explains.In addition, they applied different modulation frequencies to the vertical and horizontal signals, further reducing interference.<h2>Precise location estimation</h2>This dual-polarization and dual-modulation architecture gives the drone’s spatial location. But drones also move at an angle and rotate, so to enable a drone to navigate, it must estimate its position in space with respect to six degrees of freedom — with trajectory data including pitch, yaw, and roll in addition to the usual forward/backward, left/right, and up/down.“The drone rotation adds a lot of ambiguity to the millimeter wave estimates. This is a big problem because drones rotate quite a bit as they are flying,” Dodds says.They overcame these challenges by utilizing the drone’s onboard inertial measurement unit, a sensor that measures acceleration as well as changes in altitude and attitude. By fusing this information with the millimeter wave measurements reflected by the tag, they enable MiFly to estimate the full six-degree-of-freedom pose of the drone in only a few milliseconds.They tested a MiFly-equipped drone in several indoor environments, including their lab, the flight space at MIT, and the dim tunnels beneath the campus buildings. The system achieved high accuracy consistently across all environments, localizing the drone to within 7 centimeters in many experiments.In addition, the system was nearly as accurate in situations where the tag was blocked from the drone’s view. They achieved reliable localization estimates up to 6 meters from the tag.That distance could be extended in the future with the use of additional hardware, such as high-power amplifiers, or by improving the radar and antenna design. The researchers also plan to conduct further research by incorporating MiFly into an autonomous navigation system. This could enable a drone to decide where to fly and execute a flight path using millimeter wave technology.“The infrastructure and localization algorithms we build up for this work are a strong foundation to go on and make them more robust to enable diverse commercial applications,” Lam says.

MIT researchers developed a system that enables a drone to determine its position in 6D space in indoor, dark, or low-visibility environments using radio frequency waves. The drone has 2 radars; the horizontal radar has a horizontal polarization, which means it sends signals horizontally, while the vertical radar would have a vertical polarization. Image: Figures courtesy the researchers; edited by MIT News

A new low-power system using radio frequency waves takes a major step toward autonomous, indoor drone navigation.

Engineers enable a drone to determine its position in the dark and indoors

By <a href="https://www.linkedin.com/in/scottsimmie/" target="_blank">Scott Simmie</a>During a recent trip to California, I had the opportunity to ride in a Waymo.I’d certainly read about Alphabet’s autonomous car-for-hire service in the past and work for a company that builds robots and autonomy software. So it seemed a natural, while in San Francisco, to download the app (similar to Uber) and hail an autonomous vehicle.Within a couple of minutes, a Waymo vehicle arrived at the pickup point – just a short walk from where it had been summoned. It pulled up, LiDARs spinning, waiting for me to climb in. I put a hand on the door; it was locked. A quick glance at the app and I saw an “unlock” feature. Then I was inside.And then, with some ambient music playing in the background (you have the option to turn it off or select something else), we – meaning the car and I – were off. A display showed a digital representation of what the vehicle was seeing in its surroundings, including parked vehicles and pedestrians. The electric Jaguar quietly accelerated to the speed limit, obeyed all traffic rules, and smoothly adjusted for unexpected occurrences. When the driver of a parked vehicle opened the door to exit, the Waymo liquidly arced a safe distance away. With the steering wheel making smooth turns and constant smaller fine adjustments, it was like being in a vehicle with an invisible, silent driver at the helm.I had full faith in the technology and actually preferred it to a standard rideshare. Waymo’s safety data (which we’ll explore later) had reassured me the drive was going to be statistically safer than riding with a human driver. Plus, there was no need to engage in small talk. When the ride was done, I simply exited without being prompted for a tip.I’d been aware of Waymo since it first deployed. I also have a friend with a Tesla who has the Full Self-Driving package. He commutes twice a week from well outside Toronto into the GTA without any inputs beyond setting his destination. He foresees a day, long promised by Elon Musk, when his own vehicle will earn him money by working during off hours as an autonomous taxi.The technology for fully autonomous vehicles is basically here – arriving both sooner and later than some had predicted. But what does that mean for the future?Below: A Waymo Driver waits patiently at an intersection – while another Waymo Driver glides past. Photos by Scott Simmie&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740417936633-Waymo-intersection-pedestrian-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Inside the Waymo"><h3 id="isPasted">Sense, Solve, Go</h3>Owned by Google parent company Alphabet, Waymo states “We’re on a mission to be the world’s most trusted driver. Making it safer, more accessible, and more sustainable to get around — without the need for anyone in the driver’s seat.” It calls its service, the app and the car together, Waymo One. The system, the hardware and software, are referred to collectively as Waymo Driver.Commercial rollout began in Phoenix, Arizona in 2020, with testing in San Francisco commencing late the following year. It’s now also operating in Los Angeles, with expansion into Atlanta, Austin and Miami next. It uses a Jaguar I-PACE electric vehicle as its base, heavily outfitted with an array of sensors. We’re talking a lot&nbsp;of sensors.In total there are 29 cameras, six radar and five LiDAR units (including a roof-mounted 360° LiDAR). These sensors allow Waymo Driver to fully capture its environment up to three football fields away. Powerful AI, machine vision and machine learning software continuously crunch predictive algorithms allowing Waymo to understand where a pedestrian, cyclist or other vehicle will most likely continue based on current trajectory. And, of course, if one of those moving subjects suddenly does something unexpected/unpredicted, the system quickly readjusts. Waymo touts safety stats (which we’ll explore later) it says proves Waymo Driver is far safer than a human driver.Waymo boils down the entire process to three words: Sense, solve, go. Waymo Driver senses its environment using the sensors mentioned above (it also has an array of External Audio Receivers – EARs – which alert the system if they detect sirens, etc. It can even echolocate the location of said sirens and will understand if it needs to pull over). The algorithms solve the challenge of safely moving through that environment, including moving objects, and then it’s go time. Those three simple words represent a decade and a half of intense R&amp;D, development of its own sensors, and a huge capital expenditure.Google first began exploring self-driving vehicles in earnest back in January of 2009. By the time it revealed this publicly, it had already done extensive R&amp;D and testing. But it wasn’t until the fall of 2015 that the first solo member of the public climbed into a Waymo in Austin, Texas and took the vehicle for a ride on city streets. That passenger was Steve Mahan, who is legally blind. It was the first time in 12 years that he’d been alone in a car. It would be another five years before the first rollout to the public.During that five years, both the car and sensor package – along with the software – evolved considerably. Just compare videos below; the first shows Steve Mahan on <a href="https://medium.com/waymo/scenes-from-the-street-5bb77046d7ce#.i0scjibw5" target="_blank">that historic trip in 2015</a>, the second is an updated video explaining the fifth-generation Waymo Driver.<iframe width="640" height="360" src="https://www.youtube.com/embed/ArYTxDZzQOM??si=AknYWo_Cnm7BWQ3l&amp;wmode=opaque&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">&nbsp;&nbsp;&nbsp;</iframe><iframe width="640" height="360" src="https://www.youtube.com/embed/hA_-MkU0Nfw??si=-xznImvuhBLEyPrR&amp;wmode=opaque&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">&nbsp;&nbsp;&nbsp;</iframe><h3 id="isPasted">Safety First</h3>Waymo could pitch its offering on a number of grounds: Sustainability, convenience, cool factor. But instead, it focuses its customer-facing marketing on safety. Waymo Driver, it says repeatedly, is far safer than a human driver. It has proven that in many millions of miles on the streets, it says, and billions more in simulation.The statistics Waymo publishes are based on Incidents Per Million Miles (IPMM) of driving – and compare its own rates of incidents with a benchmark of IPMM involving human drivers. Whether it’s airbag deployments, crashes with a reported injury, or incidents where police are notified, Waymo’s stats are consistently a fraction of those involving people at the wheel. In more than 33 million miles of driving, Waymo touts these as the results:<ul><li>81 per cent fewer airbag deployment crashes</li><li>78 per cent fewer injury-causing crashes</li><li>62 per cent fewer police-reported crashes</li></ul>That’s clearly a significant reduction, and to most people would indicate that Waymo is safer than taking a ride with a stranger (or even friend) at the wheel. The statistics include accidents where other drivers were at fault, but does not separate them out – so we can’t actually see what percentage involved an error from the Waymo side. Waymo has previously stated that the majority of these incidents were the fault of human drivers, and that there have been but two accidents involving injuries where it expects to pay out insurance liability claims.But even rare incidents can quickly become high-profile. In Phoenix, an empty Waymo that had been summoned by a customer crashed at low speed into a telephone pole in an alley. Had a human been at the wheel, we would never have heard of it. But because it was a Waymo, the incident led newscasts. Why is that? Well, we expect perfection in systems like these. And that seems a reasonable expectation if you’re going to trust your personal safety to a driverless car. It just can’t make mistakes. And that’s why Waymo quickly issued a recall for a software fix.“This is our second voluntary recall,” Katherine Barna, a Waymo spokesperson, <a href="https://techcrunch.com/2024/06/12/waymo-second-robotaxi-recall-autonomous-vehicle/" target="_blank">told TechCrunch</a>. “This reflects how seriously we take our responsibility to safely deploy our technology and to transparently communicate with the public.”In May of 2024, the US National Highway Transportation Safety Agency (NHTSA) informed Waymo it was investigating 22 incidents involving its vehicles (and subsequently added an additional nine incidents), stretching back to 2001. Many of those incidents were <a href="https://www.forbes.com/sites/bradtempleton/2024/05/29/nhtsa-investigates-more-waymo-incidents-but-should-they/" target="_blank">described by Forbes</a> as “surprisingly minor” and 11 of those incidents were culled by the NHTSA from social media reports of the vehicles driving in an unusual fashion (such as using the oncoming lane to avoid traffic problems). The most serious was the aforementioned pole collision.We were unable to find any reports of Waymo incidents involving a serious injury. The one fatality involving a Waymo occurred in January of 2025, when an unoccupied stationary Waymo stopped at a traffic light was one of several vehicles hit by a speeding car. One person and a dog died in that incident, but because a Waymo was tangentially involved it <a href="https://www.nbcbayarea.com/investigations/waymo-multi-car-wreck-san-francisco-driverless/3766860/" target="_blank">made the headlines</a>. It is the only case we can find involving a fully driverless vehicle where a fatality was involved – and in this case the vehicle was completely passive. (There was a pedestrian fatality in 2018 involving an autonomous Uber vehicle. In that incident, which occurred in Tempe, Arizona, a human safety driver was in the driver’s seat. She was watching television on her phone when the accident occurred and <a href="https://www.cnn.com/2023/07/29/business/uber-self-driving-car-death-guilty/index.html" target="_blank">subsequently pleaded guilty</a> to endangerment.)While Waymo has an excellent track record, there have been incidents. But with each incident where Waymo Driver has somehow made the wrong decision, it’s reasonable to assume it was followed by a software fix. And here’s where a fleet of autonomous vehicles have a definite advantage over people: That tweak can be instantly applied to the entire fleet.Still, there are skeptics who argue that – despite those millions of miles of driverless passenger trips – Waymo does not have enough data upon which to draw sound conclusions.“We don’t know a lot. We know what Waymo tells us,” Philip Koopman, an expert on autonomous vehicle safety at Carnegie Mellon University, told the <a href="https://www.miamiherald.com/news/local/community/miami-dade/article297065439.html" target="_blank">Miami Herald</a>. “Basically you are trusting Waymo to do the right thing.”&nbsp;<h3>The Future</h3>Autonomy is hard – and it takes time: Google and Alphabet have invested more than 15 years of continuous engineering for Waymo Driver to reach this level of technological maturity.Now, Waymo is rolling out to more cities. Remember those 672 Jaguars that had the software upgrade? They’re just a fraction of the 20,000 I-Pace vehicles Waymo signed a contract with Jaguar to purchase. Plus, the company recently announced that its six-generation vehicle – a Chinese-made electric minivan – is next up for testing and deployment. From all external appearances, Waymo shows no sign of stopping (except at red lights, of course). In 2024, it carried out four million autonomous rides – four times more than its total of trips over the previous four years. Rides in 2024 tripled to 150,000 per week. <a href="https://waymo.com/blog/2024/12/year-in-review-2024" target="_blank">And the company calculates</a> “Waymo riders helped avoid over 6 million kilograms of CO2 emissions.”That’s all great. But for any commercial enterprise, even if it’s willing to absorb costs during rollout, the ultimate test will be the bottom line. Will Waymo prove profitable?We can’t say for certain – and Waymo’s current financials are somewhat invisible to the public, as they’re bundled in with several other projects Alphabet projects. But some analysts predict Waymo, the clear leader in autonomous rideshare, will ultimately win a significant piece of the market. An analysis on Nasdaq.com predicts Waymo could prove over time to be the jewel in Alphabet’s crown.“Uber does more than 200 million rides each week,” <a href="https://www.nasdaq.com/articles/waymo-worth-5-trillion-alphabet-stock" target="_blank">states the story</a>. “Let’s let that sink in. So if autonomous rides can capture even half that market, that would mean 100 million rides per week…If Waymo can capture about one-third of the $1 trillion autonomous rides market, it could generate annual revenues of around $300 billion.” Enough, suggests the story, to double Alphabet’s stock price.That’s a big prize. And, clearly, incentive for Alphabet and Waymo to continue on the road to profitability.Below: The LCD display for rear Waymo passengers. Note the option to “pull over” if you unexpectedly need to end your ride early<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740419856848-Waymo-display-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib"> <h3 id="isPasted">InDro's Take</h3>Because we’re deeply involved in the autonomous space, we obviously take great interest in Waymo and other deployments of autonomous technologies at scale. Waymo Driver is different from most other applications, though, because it’s transporting human beings. There is very little – if any – room for error.“We can’t predict the future, but – like algorithms – can make informed predictions with available data,” observes InDro Robotics Founder and CEO Philip Reece. “Waymo appears to be heavily invested in continuously making a good safety record even better – and has the engineering and financial resources to do so. I suspect Waymo, and its competitors, are here to stay.”For more on Waymo, check out <a href="https://waymo.com/" target="_blank">its website.</a> And, if you’re in one of the growing number of cities where it operates, download the app and let Waymo Driver take the wheel.

Exploring Waymo's Innovations and Their Role in Shaping the Autonomous Driving Landscape.

Sense, Solve, Go:  Does Waymo Herald The Future of Autonomous Vehicles?

So. You want to buy a robot.&nbsp;Maybe you’re in the academia or R&amp;D fields, and you need a robot for research. Or you’re a commercial/industrial client, looking for a device to carry out regular maintenance inspection, surveillance, or some other repeatable data acquisition task. You might even be looking for a robot that can pick things up and move them from one location to another.&nbsp;Regardless of the use-case, you’ll have a budget. And you’ll want to make the most of it.&nbsp;The question is: How, precisely, do you do that? How can you ensure you’re getting the best bang – and robot – for the buck?At InDro, we take pride in how we work with clients to ensure they’re maximising their spend while we minimise their pain. Regardless of where you ultimately purchase a robot, we hope the tips this post reveals will help inform your choice.Below: ‘Rosie’ – a highly complex dual-manipulator robot InDro built for a client<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740415359859-Rosie.png.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Roșie Mobile Manipulation Robot"> <h3 id="isPasted">Buy Only What You Need</h3>It’s a big world out there. And there’s more than one place to go robot shopping. But many suppliers lock clients into “packages” – which include a fixed number of sensors. Those packages tend to be rigid, even if the client might already have some of the desired components.“They tend to bundle in components, be it expensive cameras or sensors or compute that the client already has,” explains Head of R&amp;D Sales Luke Corbeth.At InDro, he says, there’s an important difference.“What we’re able to uniquely do is custom outfit a robot to only include what they need – so they don’t need to repurchase a 3D LiDAR or, say, a depth camera.&nbsp;If someone says, ‘Oh, I want this package but don’t need an IMU because I already have one,’ then we can bump down the cost and just not include the IMU.”&nbsp;<h3>Build Modular&nbsp;</h3>Needs evolve over time – as does technology. So it’s quite likely, during the lifespan of your robot, that more powerful sensors will be released. Or perhaps your own needs will change, and you realise that adding a thermal camera would really benefit a new use-case. That’s where <a href="https://indrorobotics.ca/indro-commander-module-streamlines-robotics-rd/" target="_blank">InDro Commander</a> (and <a href="https://indrorobotics.ca/the-new-indro-command-module-amazing-power-in-a-tiny-package/" target="_blank">InDro Cortex</a>, which we’ll touch on later) come in.Pretty much every robot we ship comes with a version of InDro Commander or the more compact InDro Backpack. Both of these boxes instantly transform any platform into a smart robot.How do they do that? The simple explanation is that Commander and InDro Backpack are a Plug &amp; Play hub that combines powerful compute and remote teleoperation mission planning capabilities via InDro Controller with instant sensor integration. There are a couple of features here that are key.InDro Controller is a secure, user-friendly remote mission planner. It allows you to plan repeatable tasks on complex missions with a few clicks. Want to zoom in on that gauge to ensure normal operating parameters aren’t being exceeded? No problem. Manually carry out that operation just once and InDro Controller will remember it for next time. (Oh, and InDro Controller is regularly updated.)But the real key is Commander’s future-proofing. Clients can add sensors simply by plugging them into one of Commander’s multiple USB slots. The onboard Robot Operating System library will automatically detect the new sensor and add it to the InDro Controller dashboard. Clients don’t need to do any coding or soldering to ensure the perfect fit. (Plus, we have multiple autonomy software stacks, also continuously updated, that can be added.)The ease of sensor integration with InDro Commander and InDro Controller means the client can modify or otherwise update their robot with additional or newer sensors without the hassle – and time and cost – normally associated with the task.Below: InDro’s Commander – ready for anything you can throw at it<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740415925739-InDro+Commander+1.3.png" style="width: 100%px;" class="fr-fic fr-dib" alt="InDro Commander"><h3 id="isPasted">Buy Quality</h3>You know that old maxim: You get what you pay for.That’s usually true. But sometimes, as we’ve all experienced, you can overpay. And this is particularly true in the world of robotics. With some LiDAR units costing many tens of thousands of dollars, you’ll want to be absolutely certain you’re getting the best value (as with any other sensor). But with so many choices out there, how is a customer to discern what, truly, is ‘best’? It’s not like there’s a Rotten Tomatoes for LiDAR out there.In the case of InDro, we’ve been building custom robots – for ourselves and our clients – for a long time. We’ve integrated and tested sensors from all the major manufacturers. And we’ve learned a lot during that process.“We’ve built so many robots – and we’ve used pretty much every available LiDAR, depth camera, etc. on the market along the way,” says Corbeth. “As a result,&nbsp;what we offer in terms of components is the direct result of what we’ve seen have the best performance per dollar. We also look at: What’s easiest to work with from a development perspective? These criteria are all factored into our decisions on who we partner with.”While Luke isn’t a trained engineer, he has a deep understanding of client needs, available solutions – and engineering in general. But when he’s got a question on behalf of a client, he also has our core R&amp;D engineering team to tap on. We also have InDro Forge, our fabrication and manufacturing facility in Ottawa, for building custom enclosures and integration. This allows us full quality control, infinite customisation capabilities, and speeds the process of getting a finished robot out the door without having anything done by a third party.&nbsp;<h3>After The Sale</h3>InDro has built a reputation not only for its robots, but for its support of everything we sell. If clients ever have an issue, our engineering team gets to work. We have three well-established tiers of support, shown here in order of escalation:<ul><li>Remote troubleshooting</li><li>Dispatching a Field Engineer to the client’s location</li><li>Return to the robot to InDro for repair</li></ul>“I would say in 90 per cent of cases we can we can remotely solve any issues – especially if it has <a href="https://indrorobotics.ca/new-indro-controller-a-simple-solution-to-complex-robotics-missions/" target="_blank">InDro Controller</a>. That makes it really easy to troubleshoot and update,” says Corbeth.“But if it doesn’t, then sometimes it may make sense to go in-person and do those changes on behalf of the client on site.”&nbsp;<h3>Cortex</h3>Because we’re a R&amp;D company, we are constantly looking for ways to enhance our offerings. And while Commander is still both current and powerful, we’ve put considerable resources into making it even better – and much smaller.InDro Cortex is the next generation of Commander, in a form factor so small it can easily be added to any platform: Wheeled, quadruped, humanoid – even a drone. With powerful compute, ROS1 and ROS2 libraries, InDro Controller and USB slots for sensors onboard, it’s ready for any mission. And, like Commander, InDro Cortex is a way of future-proofing your robot. If you’ve opted for InDro Controller or one of several autonomy stack options, you’ll always be working with the latest version of the software (in addition to being able to easily integrate new sensors).“Cortex is the natural progression of Commander,” says Corbeth. “But because of the way it’s structured, it’s going to cost a lot less. So we’ll be able to outfit a robot with compute, sensors and connectivity at a fraction of the cost – which is really exciting.”It is. And we look forward to a full commercial release of Cortex (which will also be available as a standalone product) by early summer. You can have a sneak peek, for free, below:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740416467600-DSCF7893-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="InDro Command Module"><h3 id="isPasted">InDro's Take</h3>If purchasing a robot is on your to-do list, we’d obviously like for you to consider InDro. But even if you don’t, we hope you’ll look for a supplier who offers everything we do, including:<ul><li>Track record with satisfied customers</li><li>Components that have been field-tested for quality and value</li><li>In-house manufacturing expertise</li><li>Modular integration ease with after-sale support</li></ul>The addition of InDro Forge to our capacity cannot be understated. We are now capable of all aspects of production, including IP-rated custom metal work for enclosures.“Buying a robot is generally a significant capital expenditure,” says InDro Founder and CEO Philip Reece. “Our background as a research and development company, where we have invented and built so many robots from scratch, has taught us how to produce the absolute best products we can for the money. Those learnings – and there have been many – now benefit our clients.”And, for those clients who know precisely what components or platforms they need, there’s also the new <a href="https://store.indrorobotics.com/" target="_blank">InDro Store</a>. Happy shopping!

How to Maximize Your Robot Without Blowing Your Budget

Building a Robot on a Budget

LiDAR (Light Detection and Ranging) technology has been making significant inroads into the industrial automation sector, enabling increased efficiency, quality, and cost savings. Hamamatsu Photonics is at the forefront of this technological advancement, offering high-quality sources and detectors for industrial LiDAR applications, and pushing their performance limits to match future industrial needs.The level of automation in industry closely follows technological progress. Industries are frequently the first to adopt new technologies with the resources at their disposal. The many benefits offered by LiDAR make the adoption of this new technology attractive.<a href="https://www.hamamatsu.com/eu/en/applications/automotive/lidar.html" id="isPasted">LiDAR</a> has penetrated deep into industrial automation. It operates by measuring the time it takes for light to travel from a source to an object, and back to the source. This allows the distance of the object from the light source to be calculated.The technology is utilized in various industrial applications including distance measurement, obstacle detection by automated guided vehicles (AGVs), and the operation of automatic doors. The key applications for LiDAR technology include autonomous navigation of vehicles, robotic and machine vision systems, and 3D mapping.Industries demand high levels of performance from LiDAR due to the risk factors involved in certain applications. To consider an example, malfunctioning LiDAR in AGVs that are moving heavy loads can cause significant damage to workers or property. Therefore, industrial LiDAR require high-quality components that are robust, offer excellent performance and have a longer lifespan. They must withstand a wide range of temperatures, humidity, dust, ambient light, and impulsive forces over extended periods while maintaining consistent performance and reliability.<hr /><a href="https://www.hamamatsu-news.de/promo/24_photonics_innovation_award/" target="_blank" rel="nofollow">The Photonics Innovation Awards 2025</a> is a critical industry event celebrating cutting-edge advancements and innovation in the field of photonics. As engineers continue to push the boundaries of what's possible with light, these awards created by Hamamatsu Photonics shine a spotlight on those who drive innovation and inspire the photonics industry.<a href="https://www.wevolver.com/article/calling-all-photonics-innovators-launch-of-the-photonics-innovation-awards-2025" target="_self"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742806220338-eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQwMDUxNzAxOTgyLTJkX0hhbV9XV19hcnRpY2xlX1BJQV8xMjAweDY3OCAoMSkuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ%3D%3D.webp" class="fr-fic fr-dii" /></a><h3><a href="https://www.wevolver.com/article/calling-all-photonics-innovators-launch-of-the-photonics-innovation-awards-2025" class="button-main-action">LEARN MORE AND SUBMIT NOW</a></h3><hr />Hamamatsu is renowned for the quality of its products and is a trusted manufacturer and supplier of LiDAR solutions for industrial applications. Through continuous innovations in active material composition, photonic design and packaging technologies, Hamamatsu pushes the performance limits of LiDAR sources. <a href="https://www.hamamatsu.com/eu/en/product/lasers/semiconductor-lasers/plds.html" id="isPasted">Pulse laser diodes (PLDs)</a> and detectors like <a href="https://www.hamamatsu.com/eu/en/product/optical-sensors/distance-position-sensor/lidar-sensor/si-apd-for-lidar.html">avalanche photodiodes (APDs)</a> and <a href="https://www.hamamatsu.com/eu/en/product/optical-sensors/%20mppc/mppc_mppc-array.html">silicon photomultipliers (SiPMs)</a> are key to this technology. The current generation of PLDs offers improved energy efficiency and smaller package sizes, with operating frequencies greater than 100s of KHz, emitting pulses shorter than 10 ns and peak powers greater than 100 W. Hamamatsu’s PLDs come in a standard TO-can or ceramic package and among the applications for these are distance measuring instruments and laser range finders.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQwMTI1NDg1ODM0LWhhbWFtYXRzdS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />They are also useful in security environments with the need for hazard monitoring. There are other options, such as the innovative SMD (Surface Mount Design) package, with resin encapsulation. The SMD package has a smaller footprint and it can be used in place of PLD based on VCSEL design. There are several customization options, including module solutions. Complementing the PLDs are a wide range of fast detectors like APDs and SiPMs, also known as <a href="https://www.hamamatsu.com/eu/en/product/optical-sensors/mppc/what_is_mppc.html" id="isPasted">multipixel photon counters (MPPC®s)</a>. These detectors are available as a single sensor or as arrays. Like the PLDs, the detectors are also available in multiple packaging options and module solutions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQwMTI1NjczNzkxLXB1bHNlZC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />One of many Hamamatsu Photonics' pulsed laser diodes (PLD).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQwMTI1NjgyOTQyLXBob3RvLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" />Hamamatsu photosensor S15597-01CT including a Si APD<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQwMTI1NjkyNTk2LWFycmF5LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" />Hamamatsu photosensor S14137-01CR including a 16-element APD array.Industrial applications often have different requirements, including package type, power supply, mechanical characteristics, and data handling. With these many variables, it can be difficult to find the right off-the-shelf solution. Hamamatsu’s engineers are readily available to support customers in finding the ideal solution for their LiDAR application and discuss any customization requirements they may have.For more information visit our website <a href="http://www.hamamatsu.com">www.hamamatsu.com</a> or email info@hamamatsu.eu.<h2>References:</h2>[1] https://www.hamamatsu.com/eu/en/applications/automotive/lidar.html [2] https://www.hamamatsu.com/eu/en/product/lasers/semiconductor-lasers/plds.html [3] https://www.hamamatsu.com/eu/en/product/optical-sensors/distance-position-sensor/lidar-sensor/si-apd-for-lidar.html [4] https://www.hamamatsu.com/eu/en/product/optical-sensors/ mppc/mppc_mppc-array.html 2|2 [5] https://www.hamamatsu.com/eu/en/product/optical-sensors/ mppc/what_is_mppc.html [6] https://www.hamamatsu.com/eu/en/product/optical-sensors/ distance-position-sensor/lidar-sensor/photosensor-with-frontend-ic/S14137-01CR.html [7] https://www.hamamatsu.com/eu/en/product/optical-sensors/ distance-position-sensor/lidar-sensor/photosensor-with-frontend-ic/S15597-01CT.html

LiDAR technology is advancing industrial automation by providing precise measurements of object distances using light, with companies like Hamamatsu Photonics leading in the development of high-quality sources and detectors.

LiDAR: making significant inroads into our factories and warehouses

Drone shows are an increasingly popular form of large-scale light display. These shows incorporate hundreds to thousands of airborne bots, each programmed to fly in paths that together form intricate shapes and patterns across the sky. When they go as planned, drone shows can be spectacular. But when one or more drones malfunction, as has happened recently in Florida, New York, and elsewhere, they can be a serious hazard to spectators on the ground.Drone show accidents highlight the challenges of maintaining safety in what engineers call “multiagent systems” — systems of multiple coordinated, collaborative, and computer-programmed agents, such as robots, drones, and self-driving cars.Now, a team of MIT engineers has developed a training method for multiagent systems that can guarantee&nbsp;their safe operation in crowded environments. The researchers found that once the method is used to train a small number of agents, the safety margins and controls learned by those agents can automatically scale to any larger number of agents, in a way that ensures the safety of the system as a whole.In real-world demonstrations, the team trained a small number of palm-sized drones to safely carry out different objectives, from simultaneously switching positions midflight to landing on designated moving vehicles on the ground. In simulations, the researchers showed that the same programs, trained on a few drones, could be copied and scaled up to thousands of drones, enabling a large system of agents to safely accomplish the same tasks.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1738629986915-ezgif-64b5c2f01d348.gif" style="width: 100%px;" class="fr-fic fr-dib">A team of Crazyflie drones use MIT algorithm to safely switch positions while avoiding obstacles. Image: Courtesy of the researchers“This could be a standard for any application that requires a team of agents, such as warehouse robots, search-and-rescue drones, and self-driving cars,” says Chuchu Fan, associate professor of aeronautics and astronautics at MIT. “This provides a shield, or safety filter, saying each agent can continue with their mission, and we’ll tell you how to be safe.”Fan and her colleagues report on their new method in a study&nbsp;<a href="https://dspace.mit.edu/handle/1721.1/158072" target="_blank">appearing this month in the journal&nbsp;IEEE Transactions on Robotics</a>. The study’s co-authors are MIT graduate students Songyuan Zhang and Oswin So as well as former MIT postdoc Kunal Garg, who is now an assistant professor at Arizona State University.<h2>Mall margins</h2>When engineers design for safety in any multiagent system, they typically have to consider the potential paths of every single agent with respect to every other agent in the system. This pair-wise path-planning is a time-consuming and computationally expensive process. And even then, safety is not guaranteed.“In a drone show, each drone is given a specific trajectory — a set of waypoints and a set of times — and then they essentially close their eyes and follow the plan,” says Zhang, the study’s lead author. “Since they only know where they have to be and at what time, if there are unexpected things that happen, they don’t know how to adapt.”The MIT team looked instead to develop a method to train a small number of agents to maneuver safely, in a way that could efficiently scale to any number of agents in the system. And, rather than plan specific paths for individual agents, the method would enable agents to continually map their safety margins, or boundaries beyond which they might be unsafe. An agent could then take any number of paths to accomplish its task, as long as it stays within its safety margins.In some sense, the team says the method is similar to how humans intuitively navigate their surroundings.“Say you’re in a really crowded shopping mall,” So explains. “You don’t care about anyone beyond the people who are in your immediate neighborhood, like the 5 meters surrounding you, in terms of getting around safely and not bumping into anyone. Our work takes a similar local approach.”<h2>Safety barrier</h2>In their new study, the team presents their method, GCBF+, which stands for “Graph Control Barrier Function.” A barrier function is a mathematical term used in robotics that calculates a sort of safety barrier, or a boundary beyond which an agent has a high probability of being unsafe. For any given agent, this safety zone can change moment to moment, as the agent moves among other agents that are themselves moving within the system.When designers calculate barrier functions for any one agent in a multiagent system, they typically have to take into account the potential paths and interactions with every other agent in the system. Instead, the MIT team’s method calculates the safety zones of just a handful of agents, in a way that is accurate enough to represent the dynamics of many more agents in the system.“Then we can sort of copy-paste this barrier function for every single agent, and then suddenly we have a graph of safety zones that works for any number of agents in the system,” So says.To calculate an agent’s barrier function, the team’s method first takes into account an agent’s “sensing radius,” or how much of the surroundings an agent can observe, depending on its sensor capabilities. Just as in the shopping mall analogy, the researchers assume that the agent only cares about the agents that are within its sensing radius, in terms of keeping safe and avoiding collisions with those agents.Then, using computer models that capture an agent’s particular mechanical capabilities and limits, the team simulates a “controller,” or a set of instructions for how the agent and a handful of similar agents should move around. They then run simulations of multiple agents moving along certain trajectories, and record whether and how they collide or otherwise interact.“Once we have these trajectories, we can compute some laws that we want to minimize, like say, how many safety violations we have in the current controller,” Zhang says. “Then we update the controller to be safer.”In this way, a controller can be programmed into actual agents, which would enable them to continually map their safety zone based on any other agents they can sense in their immediate surroundings, and then move within that safety zone to accomplish their task.“Our controller is reactive,” Fan says. “We don’t preplan a path beforehand. Our controller is constantly taking in information about where an agent is going, what is its velocity, how fast other drones are going. It’s using all this information to come up with a plan on the fly and it’s replanning every time. So, if the situation changes, it’s always able to adapt to stay safe.”The team demonstrated GCBF+ on a system of eight Crazyflies — lightweight, palm-sized quadrotor drones that they tasked with flying and switching positions in midair. If the drones were to do so by taking the straightest path, they would surely collide. But after training with the team’s method, the drones were able to make real-time adjustments to maneuver around each other, keeping within their respective safety zones, to successfully switch positions on the fly.In similar fashion, the team tasked the drones with flying around, then landing on specific Turtlebots — wheeled robots with shell-like tops. The Turtlebots drove continuously around in a large circle, and the Crazyflies were able to avoid colliding with each other as they made their landings.“Using our framework, we only need to give the drones their destinations instead of the whole collision-free trajectory, and the drones can figure out how to arrive at their destinations without collision themselves,” says Fan, who envisions the method could be applied to any multiagent system to guarantee its safety, including collision avoidance systems in drone shows, warehouse robots, autonomous driving vehicles, and drone delivery systems.This work was partly supported by the U.S. National Science Foundation, MIT Lincoln Laboratory under the Safety in Aerobatic Flight Regimes (SAFR) program, and the Defence Science and Technology Agency of Singapore.

MIT engineers developed a training method for multiagent systems, such as large numbers of drones, that can guarantee their safe operation in crowded environments. Image: Courtesy of the researchers

New research could improve the safety of drone shows, warehouse robots, and self-driving cars.

Engineers help multirobot systems stay in the safety zone

In the market for a robot? A LiDAR sensor? Perhaps a Six Degree of Freedom manipulator?You’ve come to the right place. InDro Robotics is pleased to announce its <a href="https://store.indrorobotics.com/" target="_blank">new online store</a> – your one-stop portal for high quality robotic components. Whether it’s a $2500 US <a href="https://indrorobotics.ca/theres-a-new-robot-in-town-meet-limo/" target="_blank">LIMO</a> (an amazing tiny R&amp;D powerhouse) or a $80,000 <a href="https://indrorobotics.ca/robosense-sets-new-bar-for-affordable-powerful-lidar-sensors/" target="_blank">Robosense LiDAR</a>, we’ve got you covered.“We’re incredibly excited about this addition to our offerings,” says Head of R&amp;D Sales Luke Corbeth. “For clients who need a specific component and know what they want, this will streamline the process and get products into their hands more quickly.”We’re not talking here about custom builds – something InDro has built a stellar reputation on. But precisely because we’ve done so many custom builds, we’ve been able to identify best-in-class components. And while the majority of our clients come to InDro so that we can do the integration for them, there are some who want to either perform that integration themselves, or simply add a sensor or compute upgrade to an existing robot.“Through building all of these robots, we’ve had a front row seat into what works, what doesn’t work, what’s best – and what is most cost-effective,” adds Corbeth. “Over time, we’ve settled on a select few components we use in the vast majority of our builds. So the purpose of the store is that if you want a component that goes into an InDro Robotics build – up to a fully functional quadruped – you can simply order it online from us.”Below: Manipulator? LiDAR? InDro’s new online store offers a broad but highly curated selection of quality robotics components<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737391378240-store-manipulators-LiDAR-Cameras.png.webp" style="width: 100%px;" class="fr-fic fr-dib"> <h3 id="isPasted">Simplifying The Client Journey</h3>Through scores of custom builds, including robots for some of the largest tech companies in the world, we’ve identified the most robust and cost-effective components. We’ve also learned that some clients with existing robots want to simply add a sensor or upgrade their compute.For those clients who know specifically what they’re after, the online store streamlines the purchasing process. And if you’re unsure precisely which component would be optimal for your application? There are plenty of choices with LiDAR and compute, for example. No worries. Corbeth is still happy to walk you through the options and help you decide on the best choice for your application.“In a lot of cases, people will have done their research and know precisely what they want. But, absolutely, I’m always available to have a discussion and ensure they’re choosing exactly what they need,” he says.&nbsp;<h3>This is not TEMU</h3>InDro has zero interest in selling clients something they don’t need, or a component that won’t get the job done. That’s why the offerings in our store have been carefully curated. These are the components our engineers work with every single day – and which go into the custom robots we integrate for clients. Each and every item available online is a product we have confidence in, and which has been proven in the field. Our online store includes multiple options in the following categories:<ul><li>Components:&nbsp;Cameras, compute, LiDAR, mobile manipulators, wireless charging</li><li>Robots:&nbsp;GO2 quadruped, LIMO Pro, LIMO</li><li>Drones:&nbsp;Multiple offerings and add-ons from DJI</li></ul>But why not simply shop around and buy these somewhere else online? Sure, you could do that. However, you’d be missing out on the InDro after-sales support we are known for. If you have an issue with integrating a component purchased from InDro, Luke is just a phone call away. And, in the rare event he doesn’t have the answer, you have access to our brain trust of engineers at Area X.O to solve your problem. That’s not something you’ll get purchasing online from some faceless, distant distributor.Plus, clients get access to InDro’s extensive documentation on these products and their integration.“We’re going to share every resource that we use ourselves when building robots to help enable their development,” says Corbeth.And, because InDro is an authorised distributor for every component we sell, you don’t run the very real risk of purchasing a grey market product – where manufacturers tend to not honour warranties.Plus, there are some smaller items – like that tiny but powerful LIMO R&amp;D robot – where the client knows that’s exactly what they want. (We have one client who has purchased dozens of these robots.) In scenarios like that, there’s no question the online store will speed the process.“These smaller ticket items don’t need to go through the same administrative process as our larger custom robots. They can be purchased on a procurement credit card, which completely bypasses the traditional procurement process,” says Corbeth.“So, absolutely, this will be an easier way for people to do rapid transactions and get the hardware quickly.”Below: Can we give you (or sell you) a hand?<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737391445264-Store-compute-grippers.png.webp" style="width: 100%px;" class="fr-fic fr-dib"> <h3 id="isPasted">InDro's Take&nbsp;</h3>We’re pretty excited about the new online store. The components we offer, whether it’s a Robosense LiDAR or a DJI drone, all come with the manufacturer’s warranty – along with InDro’s exhaustive documentation and legendary post-sales support.“We’re confident the store will help clients get the quality components they need as quickly as possible,” says InDro Robotics Founder and CEO Philip Reece. “I wouldn’t call it the Amazon of robotics – at least not yet – but it will definitely simplify the purchasing process for those clients who know specifically what they want, and get those products in their hands more quickly.”The look and feel of the store was conceived by Head of Marketing Carli Parkinson – who worked closely with Luke Corbeth and our engineering staff to select the absolute best range of robotic components (and even robots) for its inventory.There’s already a robust selection online. But, of course, as newer specialised products become available and we integrate and test them in our own robots, they’ll be added to the lineup.We encourage you to take a <a href="https://store.indrorobotics.com/" target="_blank">spin through the store now</a>. And, if you’re ready to purchase but have any questions, <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank">contact us here.</a> Happy shopping!

InDro Launches New Online Store

Robotics Components in a Click

The rapid evolution of advanced driver assistance systems (ADAS) increases the complexity and demand for real-time data processing, which requires high-performance processors to handle tasks such as object recognition, sensor fusion and decision-making. This increased complexity introduces new challenges on the power-management side. Starting with the vehicle’s battery (12V, 24V or 48V), a preregulator steps down the battery voltage to feed point-of-load (PoL) buck converters, supplying the processor with the precise voltages needed for optimal operation. Additionally, there is strong focus on meeting functional safety requirements for the entire system, with the power stage playing a critical role to enable meeting these requirements.Solutions such as the 80V <a href="https://www.ti.com/product/LM5137F-Q1?HQS=app-sr-pwr-density_asil-bucks-vanity-pf-lm5137fq1-ww_en" target="_blank">LM5137F-Q1</a> dual-channel <a href="https://www.ti.com/lit/pdf/ssztd62?HQS=app-sr-pwr-density_asil-bucks-vanity-ta-ssztd62-ww_en" target="_blank">buck controller</a> and the 6V, 30A <a href="https://www.ti.com/product/TPS62883-Q1?HQS=app-sr-pwr-density_asil-bucks-vanity-pf-tps62883q1-ww_en" target="_blank">TPS62883-Q1</a> dual-phase buck converter provide power to high-current automotive processors while helping systems achieve functional safety compliance up to Automotive Safety Integrity Level (ASIL) D.<h2>Enabling up to ASIL D systems requirements</h2>The <a href="https://www.ti.com/product/TPS62883-Q1?HQS=app-sr-pwr-density_asil-bucks-vanity-pf-tps62883q1-ww_en" target="_blank">TPS62883-Q1</a> is a TI Functional Safety-Compliant 30A buck converter developed according to International Organization for Standardization (ISO) 26262. The device is part of a scalable product family offering pin-compatible options with current ratings from 12A to 30A, and supports a stackable architecture to deliver loads beyond 100A, enabling the power requirements for modern ADAS systems-on-a-chip (SoCs) where the demand for higher currents is ever-increasing. <a href="https://www.ti.com/document-viewer/lit/html/SSZTD61#FIG_F1H_131_CDC">Figure 1</a> shows two TPS62883-Q1 converters in a stacked configuration.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737127460065-Screenshot+2025-01-17+162345.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 1. Two TPS62883-Q1 devices in a stacked configurationThe TPS62833-Q1 helps remove external output voltage supervision through safety-compliant internal monitoring. Also, a change in the output voltage through I2C takes effect at the same time, for both the output voltage control loop as well as the internal output voltage monitoring. This feature eliminates blind spots in the output voltage monitoring during dynamic or adaptive voltage scaling. The communication through the I2C-compatible interface can be cyclic redundancy check (CRC) protected so that the MCU can detect and flag bit errors in bus communication.&nbsp;Also, the TPS62883-Q1 provides warnings based on its thermal-warning threshold and enters a safe state in case of critical fault conditions in the system such as undervoltage or overvoltage lockout. The device also features a nINT fault pin that signals to the microcontroller (MCU) when a fault condition occurs, which can then take the necessary action depending on the application requirements.<h3>Improving load transient response for ADAS processor applications&nbsp;</h3>Meeting high current requirements is not the only challenge in power supplies. In fact, load transient response is essential for maintaining reliable operation of high-performance processors. Rapid changes in processing demand cause sudden changes in power consumption, leading to voltage undershoots and overshoots, potentially triggering malfunctions. A fast load-transient response is crucial for reliable operation of these SoCs.&nbsp;SoCs specify a defined tolerance for each of their supply voltages. The core voltage rail is the most important one because the dynamics in supply currents are high, the voltage is low, and tolerance is in the order of millivolts.&nbsp;The total budget for the load transient depends on several factors, as illustrated in Figure 2. In general, you want a large remaining window for the load-transient voltage deviation, as this allows lower output capacitance. The TPS62883-Q1 provides ±0.5% DC output voltage accuracy as well as internal voltage monitoring, which removes the additional tolerance introduced by external voltage supervision.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737127572240-Screenshot+2025-01-17+162605.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 2. Load transient budget based on core-supply voltage tolerancesThe TPS62883-Q1 also incorporates flexible droop compensation, allowing you to tailor the droop behavior closely to the requirements of your design. This feature scales the nominal output voltage based on the output current, such that the output voltage scales linearly between zero and full output current.&nbsp;As shown in Figure 3, the output voltage is set slightly higher than its nominal value at a no-load condition and slightly lower than its nominal value at a maximum load condition. Droop compensation therefore helps keep the output voltage within a certain tolerance band in case of a heavy load step or at a load release.&nbsp;Load transient Vout ripple Additionally, the dual-phase design of the TPS62883-Q1 provides higher loop bandwidth and significantly lower output voltage ripple compared to single-phase designs, offering extra headroom for load transients. The combination of these features enables you to reduce the amount of output capacitance and meet stringent load-transient requirements, while cutting costs and reducing overall system complexity.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737127991867-Screenshot+2025-01-17+162903.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 3. Voltage scaling with output currentTo help streamline your functional safety system-level certification, TI provides industry-standard reports and additional resources – including IC-level documentation such as functional safety FIT rate and failure-mode effects and diagnostic analysis (FMEDA).<h2>Conclusion</h2>The complexity of ADAS continues to increase significantly as vehicles progress toward autonomous driving, resulting in greater power demands for application processors. Concurrently, functional safety is paramount, as these systems need to be reliable for users to depend on. The TPS62883-Q1, when used in a stackable configuration, achieves core power beyond 100A and in parallel helps enable system designs to achieve functional safety compliance as high as ASIL D.<h3>Additional resources&nbsp;</h3>Download the <a href="https://www.ti.com/product/TPS62883-Q1?HQS=app-sr-pwr-density_asil-bucks-vanity-pf-tps62883q1-ww_en?">TPS62883-Q1 data sheet.</a>Check out the<a href="https://www.ti.com/tool/TPS62883EVM-010?HQS=app-sr-pwr-density_asil-bucks-vanity-evm-tps62883evm010-ww_en?">&nbsp;"TPS6288x-Q1 buck converter evaluation module" user's guide.&nbsp;</a>Read the technical article,<a href="https://www.ti.com/lit/pdf/ssztd62?HQS=app-sr-pwr-density_asil-bucks-vanity-ta-ssztd62-ww_en?">&nbsp;“Achieving functional safety compliance in automotive off-battery buck preregulator designs."</a>Learn more about stackability in the&nbsp;<a href="https://www.ti.com/lit/an/slvafh4/slvafh4.pdf?ts=1729175601601&ref_url=https%253A%252F%252Fwww.ti.com%252Fproduct%252FTPS62873-Q1?" data-lf-fd-inspected-kn9eq4roawkarlvp="true">"Operating TPS6287X-Q1 Devices in a Stacked Configuration" application note. </a>Trademarks All trademarks are the property of their respective owners.

The rapid evolution of ADAS boosts demand for real-time processing and complex power management, requiring high-performance processors and advanced solutions like the 80V LM5137F-Q1 and 6V, 30A TPS62883-Q1 buck converters to ensure optimal operation and meet top safety standards like ASIL D.

Powering next-generation ADAS processors with TI Functional Safety-Compliant buck regulators

For autonomous vehicles (AVs) to travel safely, they require up-to-date, high-definition maps that accurately reflect their changing surroundings. If not continually revised, these maps quickly become outdated, increasing the risk of accidents and stalling the development of fully self-driving vehicles.&nbsp;Dr. Gaurav Pandey, associate professor in the Department of Engineering Technology and Industrial Distribution (ETID), received a grant from Ford Motor Company to push the boundaries of current technology. &nbsp;At his <a href="https://sites.google.com/tamu.edu/cvrl/">Computer Vision and Robotics Lab</a> in ETID’s Multidisciplinary Engineering Technology program, Pandey is focused on creating the most optimal maps in the most optimal way to make highly and fully automated transportation part of daily life.&nbsp;Some people believe AVs can safely navigate from point A to point B relying solely on cameras, just as humans use their eyes. However, Pandey points out that humans utilize their vision in conjunction with mental maps formed in their minds. These mental maps are automatically updated when conditions change, such as during construction. Today’s mapping technology for AVs is costly and offers no easy way to update in real-time.&nbsp;To advance this technology, Pandey is developing a software framework for crowd-sourced 3D map generation and visual localization from camera data. The crowd-sourced data will come from the cameras and sensors of Ford vehicles already on the road, leveraging information that is automatically being collected instead of employing specialized mapping cars that travel around to gather data.&nbsp;Pandey will use the crowd-sourced data to create new mapping capabilities that allow real-time updates and aid low-cost visual localization. The intent of this technology is to improve autonomous driving and, ultimately, put fully self-driving vehicles on the road.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1736902897726-News-ETID-WhiteCar-10Jan2025.jpg" style="width: 100%px;" class="fr-fic fr-dib">AV mapping vehicles use multiple sensors and cameras. | Image:&nbsp;<cite id="isPasted">Courtesy of Ford Motor Company.</cite>The Society of Automotive Engineers defines six levels of autonomy, from none at level zero to full at level five. Many of today’s new vehicles are level two, offering driver assistance such as self-parking and speed control. Currently sold by only one company, level three vehicles allow the driver to remove their hands from the steering wheel while remaining ready to assist if needed. Level four needs no driver interaction except in some weather conditions. The eventual goal is level five, at which no human intervention is necessary.&nbsp;Predictions vary regarding when level four and five AVs will be available for consumer purchase. While several companies are testing level four AVs on public roads in the U.S., only a few have them in any form of service. Currently their use is limited to driverless taxis in limited geographic areas within large cities. Waymo, owned by Google’s Alphabet, operates AV taxis in the San Francisco Bay Area, Los Angeles County and Metro Phoenix. Amazon’s Zoox carried its first passengers in California last year and announced it will begin testing its robotaxis in Austin and Miami. Other companies are in the testing phase of their driverless taxis.Level four and five vehicles are expected to increase safety for riders, surrounding vehicles, pedestrians and cyclists by eliminating human error. They will give their riders free time to work or relax. As a licensed driver will not be required, they may be of great benefit to those with disabilities that make driving difficult or impossible.&nbsp;Pandey brings over 10 years of experience in AV research to the goal of making self-driving vehicles a common reality in our world. He says we are already witnessing the use of AVs in various controlled environments, such as in mining and ports, in addition to the AV taxis. For him, it is no longer a question of "if" we will see full automation integrated into all aspects of our lives, but rather "when" it will be implemented at scale.

An autonomous vehicle localized within a 3D map. | Image: Courtesy of Ford Motor Company.

Texas A&M University is creating advanced mapping technologies to further the evolution of autonomous vehicles.

Navigating a Safer Path for Autonomous Vehicles

In this episode, we recap our favorite episodes from 2024!

2024 Recap & 2nd Annual Saucies!

If you’re in the Northern Hemisphere, Winter is Coming.And if you’re a diehard cyclist, that also means you’re going to face challenges beyond the cold. Bike paths – just like the adjacent roads – can get mucked up with potholes, ice, and (like your driveway) blocked when a snowplow makes an errant dump.In the future, cities will have a new tool at their disposal to help ensure cyclists can have the best thoroughfares possible in the depths of winter. It’s called the Street Smart Robot, and it was designed and built by the InDro Robotics engineering team at Area X.O in Ottawa. We believe it’s a great example of how robotics can help make future Smart Cities safer, more efficient, and more liveable for all of us.It was a complex build from the ground up, requiring a lot of work on Machine Vision and obstacle detection and avoidance. Here’s a sneak peak we released while development was still underway:<iframe width="640" height="360" src="https://www.youtube.com/embed/I1MTJHVsIKA??si=Bw1lA0hTFdqh2x3B&amp;wmode=opaque&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">&nbsp;</iframe><h3 id="isPasted">Funding</h3>Research and Development companies like ours are often fortunate enough to receive funding to assist with projects like these. Our concept for the Street Smart Robot caught the attention of the <a href="https://www.ovinhub.ca/" target="_blank">Ontario Vehicle Innovation Network</a> (OVIN). Its <a href="https://www.ovinhub.ca/" target="_blank">website</a> describes the agency as “Ontario’s flagship initiative for the automotive and mobility sector…driving economic development and catalyzing a future that builds safer, cleaner, and more efficient transportation.”OVIN committed $465,000 to the SSR project.<div title="Page 1">“With a world-class innovation ecosystem, Ontario continues to lead in the development of new mobility technologies and solutions,” said Vic Fedeli, Minister of Economic Development, Job Creation and Trade in our <a href="https://indrorobotics.ca/wp-content/uploads/2024/12/InDro-Robotics-Street-Smart-Robot-News-Release.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">news release</a> about the project. “InDro Robotics’ prototype Smart Street Robot represents the best of our province’s cutting-edge tech research and development that is creating solutions for real-world challenges, and we congratulate them on this milestone.”OVIN also weighed in:<div title="Page 1">“Ontario is home to innovators and game-changers that are shaping the future of mobility on the global stage. The growth of multi-modal transportation, combined with weather-related challenges, presents a unique opportunity for Ontario companies to commercialize leading-edge solutions that make our roads safer and travel more efficient,” said Raed Kadri, Head of OVIN. “This groundbreaking technology showcases the unparalleled caliber of Ontario SMEs in addressing significant mobility challenges—and more broadly, the province’s capacity to foster innovation and accelerate their time to market.”&nbsp;<h3>Networking</h3></div>Robots like the SSR require more than just good sensors and compute. They also require an exceedingly reliable 5G network in order to accommodate the dense data throughput required. Here, we partnered with <a href="https://www.rogers.com/" target="_blank">Rogers Communications</a> for networking hardware and technical support.</div><div title="Page 1">“Rogers is proud to work alongside InDro Robotics and OVIN to improve public safety,” said Tom Turner, President, <a href="https://www.rogers.com/business" target="_blank">Rogers Business</a>. “By leveraging the <a href="https://www.rogers.com/business/iot/wireless-private-network" target="_blank">Rogers 5G Hybrid Wireless Private Network</a>, we’re able to help build safer communities through real-time detection and reporting.”Robust, high-speed networks are crucial for Smart Cities – where a multiplicity of interconnected devices will be constantly relaying data. This will be especially true as more connected and autonomous vehicles like the Street Smart Robot start sharing roadways (or bicycle paths) with people and other traffic..“The Street Smart Robot is designed to greatly enhance the safety of bicycle lanes, particularly during winter. InDro is pleased to have partnered with Rogers and OVIN on this endeavour,” says InDro Robotics Founder/CEO Philip Reece.Below: The SSR incorporates LiDAR and 3D depth cameras to help sense its surroundings – and to avoid cyclists, pedestrians and obstacles<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733845991303-SSR-LiDAR-Units-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="SSR LiDAR"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733846026679-SSR-Depth-Camera-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="SSR Depth Camera"><h3 id="isPasted">InDro's Take</h3>This is actually just the beginning of the Street Smart Robot’s journey. This is the prototype version, and we are already looking at ways to further enhance the product prior to commercial deployment. We’ve learned, for example, that while the existing tread locomotion is great for heavy snow and ice that an all-wheel drive wheeled platform will provide greater agility and speed. We are also looking at a smaller and more human-friendly form factor.That being said, our SSR V1 has proven itself for Simultaneous Localisation and Mapping (SLAM), as well as detecting hazards that could cause cyclists problems.“Safer streets for everyone – including cyclists – are part of the Smart City of the future,” says InDro Robotics Founder and CEO Philip Reece. “The Street Smart Robot is an important step along that road.”We’ll have more to say about our Street Smart Robot when we release V2 in the future. So stay tuned – and, if you’re cycling, stay safe.</div>

A new robot to help ensure cyclists can have the best thoroughfares possible in the depths of winter.

InDro's Street Smart Robot Built to Keep Cyclists Safe in Winter

Nexperia addresses the challenges of ADAS integration with innovative semiconductor solutions, driving the future of safer and smarter vehicles.

ADAS and the Road to Autonomous Driving: Insights from Nexperia's Industry Expert Ahad Buksh

The scaled city, located in the basement of Hollister Hall, seeks to improve the performance of emerging mobility systems, such as connected and automated vehicles, by enabling small motorized cars to interact with their environment as well as other vehicles, some of which are piloted remotely by humans. Credit: Noël Heaney/Cornell University

A 20-by-20-foot “smart” city shrunk to 1:25 scale and its fleet of custom-built cars, drones, cameras and virtual reality technology are helping researchers design a better – and safer – future for transportation. 

Mini smart city drives design of safer automated transportation

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

autonomous vehicles

A LiDAR sensor is more compact mountable on a vehicle for driver assistance

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

Lidar vs. Radar: Comprehensive Comparison and Analysis

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

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.

Autonomous Vehicles

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

Technical Guides

Lidar vs. Radar: Comprehensive Comparison and Analysis

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

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

Driving Innovation: Harnessing IoT Potential in the Automotive Sector

Lidar SLAM: The Ultimate Guide to Simultaneous Localization and Mapping

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

Profiles