Liquid crystal elastomers are a class of soft materials that can change shape in response to stimuli such as light or heat — making them promising for applications in soft robotics, wearable and biomedical devices, smart textiles and more. But designing compositionally uniform elastomers that can change into different shapes in response to just one stimulus has been challenging and has limited the application of these potentially powerful materials.&nbsp;Now, researchers from the&nbsp;<a href="https://seas.harvard.edu/">Harvard John A. Paulson School of Engineering and Applied Sciences</a> (SEAS) have developed a way to program liquid crystal elastomers with the ability to deform in opposite directions just by heating — opening up a range of applications.&nbsp;The research was published in&nbsp;<a href="https://www.science.org/doi/10.1126/science.adq6434" target="_blank">Science</a>.&nbsp;“We have shown that by encoding multiple phase transitions within a single material, we can create scalable, easy to fabricate systems capable of achieving various deformations and shape changes without the need of complex designs or composite materials,” said&nbsp;<a href="https://seas.harvard.edu/person/joanna-aizenberg">Joanna Aizenberg</a>, the Amy Smith Berylson Professor of Materials Science and Professor of Chemistry &amp; Chemical Biology at SEAS and senior author of the paper.&nbsp;The team used an approach that combined theoretical modeling,&nbsp;chemical modifications, and mechanics of polymer-polymer interactions to design the system. They began by orienting liquid crystal monomers, which could be bonded together to form polymer chains, along one direction in a magnetic field. The monomers, which were designed with long molecular tails and long linkers, would dangle at the side of polymer chains — like the pendulum of a clock.When exposed to UV light, the oriented molecules tether together and form polymer chains. The process of polymerization contracts and puts strain on the material, squeezing those aligned monomers into a zig-zag, chevron-like pattern of polymers.&nbsp;The researchers found that when they increased the temperature, those chevron-shaped polymers flattened and stacked into flat layers. If they increased the temperature further, those layers transformed into a randomly coiled state — like a pile of spaghetti. When they lowered the temperature back down, the random polymers reformed into flat layers then back into chevrons.&nbsp;“With soft actuators, we need deformations to be reversible so we can cycle them many, many times,” said Milan Wilborn, a PhD candidate in Aizenberg’s lab and co-first author of the paper. &nbsp;“And we demonstrated that we can switch between these three phases at high frequency, moving quickly between each deformation state.”The researchers also demonstrated that by aligning the molecules in different directions before polymerization, they could encode different shape changes into the material, including flat sheets that move like the fins of stingrays and jellyfish by morphing into saddles and domes. &nbsp;“What is so powerful about this research is we have this single, soft material which can achieve opposite deformations. So at room temperature, the material is flat, then it forms a saddle when the polymers are stacked in flat layers and then transforms into a dome when the polymers become random,” said Wilborn. “We’re really excited about using these materials as soft actuators for bioinspired motions.”These transformations respond to not only temperature, but also light and solvent, making them useful for applications from smart textiles to temperature-regulating robotic skins.&nbsp;This research was co-authored by Yuxing Yao, Baptiste Lemaire, Foteini Trigka, Friedrich Stricker, Alan H. Weible, Shucong Li, Robert K. A. Bennett, Tung Chun Cheung, Alison Grinthal, Mikhail Zhernenkov, Guillaume Freychet, Patryk Wąsik, Boris Kozinsky, Michael M. Lerch and Xiaoguang Wang.It was supported by the US Department of Energy, Office of Science, Basic Energy Sciences and the Harvard University Materials Research Science and Engineering Center (MRSEC).

Researchers encode multiple shapes into simple material

Shape-changing soft material for soft robotics, smart textiles and more

<section id="isPasted"><h2>What Are Hull Inspections</h2>In the vast expanse of the ocean, where container ships traverse like modern-day superhighways, ensuring uninterrupted global trade relies significantly on the integrity of vessel hulls. The term "hull" encompasses the outer shell of a ship, subjected to constant interaction with water. Hull inspections entail meticulous examinations of this crucial component, vital for maintaining the vessel's structural soundness, operational safety, and efficiency. Whether exposed or covered with a deck, the hull plays a crucial role in the ship's framework, serving as the primary barrier against leaks.Given the extensive expanse of ship hulls and their constant interaction with water, routine hull inspections are critically important. These inspections are essential due to the diverse range of interconnected elements, scenarios, and possible defects, and are instrumental in optimizing vessel performance.</section><section><h2>Significance of Hull Inspections</h2><img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/3YwWuh97P77FJqavh93bXh/2d6802d2691a3e3d77474768018ed118/image6__3_.jpg" alt="DTG3 Ship Inspection -cta" class="fr-fic fr-dii">Hull inspections are crucial for safeguarding maritime safety and operational integrity. By meticulously examining a ship's outer structure, these inspections identify and mitigate vulnerabilities such as corrosion, fractures, and material degradation. This preventive approach not only averts potential leaks and structural failures but also enhances the safety of crew members, passengers, and the marine environment.Furthermore, adherence to regulatory standards, enhanced operational efficiency, and prolonged vessel lifespan reinforce the multifaceted importance of regular hull assessments in sustaining a reliable and environmentally responsible maritime industry.</section><section><h3>Defect Identification and Structural Integrity</h3>The hull acts as the first line of defense against water intrusion. From superficial scrapes to critical fractures, these assessments uncover various anomalies that could compromise the vessel's integrity. By detecting and addressing issues promptly, such as leaks, corrosion, or material weakening, inspectors mitigate the risk of escalating structural concerns that could jeopardize maritime safety and operational efficiency.<h3>Port Security</h3>Beyond structural considerations, hull inspections play a pivotal role in fortifying port security and mitigating potential risks. By scrutinizing the ship's exterior for anomalies, unauthorized alterations, or suspicious items, inspectors uphold security protocols and regulatory compliance. This proactive approach not only safeguards ports and coastal regions from potential threats but also emphasizes the importance of maintaining safety standards and preventing hazardous substances from infiltrating waterways.<h3>Mitigating Ecological Impact</h3>Fouling, the accumulation of marine organisms on a vessel's hull, poses ecological risks, including the introduction of invasive species and ecosystem disturbances. Regular hull inspections and maintenance endeavors are instrumental in mitigating this issue, preserving marine ecosystems, and curbing the proliferation of harmful species. Through proactive measures, such as antifouling paint assessments, operators can minimize ecological impact, uphold operational efficiency, and adhere to environmental stewardship principles.</section><section><h3>Checking Antifouling Paint</h3>Antifouling paint is applied to a ship's hull to prevent the buildup of marine organisms, such as barnacles, mussels, and algae, which can significantly impact the vessel's performance. Regular inspections ensure that the antifouling paint remains effective, preventing excessive fouling that can increase drag, reduce fuel efficiency by up to 40%, and compromise the ship's maneuverability.By examining the condition of the antifouling paint during hull inspections, ship operators can identify any degradation or growth of organisms and take timely measures to maintain optimal performance, reduce operational costs, and minimize the ecological impact on marine ecosystems.</section><section><h2>Challenges Involved in Traditional Hull Inspection Methods</h2>Traditional hull inspection methods present several notable challenges. These methods are often costly and time-consuming, involving manual inspections by divers or technicians, leading to extended downtime for vessels.Additionally, human error, limited accessibility to certain areas, and inconsistent data collection and analysis further compound the drawbacks. The costs associated with labor, equipment, and potential repairs are substantial. To address these issues, there's a growing interest in adopting advanced technologies, such as ROVs, for more efficient and comprehensive hull inspections.<h3>Time intensive procedures</h3>Conventional hull inspections demand considerable time investments. Manual assessments, involving diver or technician intervention, extend the inspection timeline, particularly for larger vessels. Factors such as scheduling diver availability, coordinating with surveyors, and obtaining necessary permits contribute to prolonged downtime for ships, disrupting operational schedules.<h3>Deployment Logistics</h3>Preparing for and deploying inspection teams, equipment, and tools can also be time-consuming. This includes assembling diving gear, positioning boats, and coordinating personnel, and it’s also important to note that ships must be disabled before working. Delays in deployment can further contribute to the overall time required for inspections.<h3>Limited Underwater Visibility</h3><img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/77NtYl6EqyShtw7MEDOMqn/8e5393a946440f848bd83c1f48182854/ROV_Looking_at_Phone.jpg" alt="DTG3 underwater murky waters" class="fr-fic fr-dii">Underwater visibility limitations present significant hurdles to accurate hull assessments. Murky water, sedimentation, and inadequate lighting obscure defect detection, potentially resulting in oversight or misinterpretation of critical issues.Compass navigation can also be difficult in harsh conditions, presenting a challenge to divers and increasing the potential risks involved. Photos and conditions are noted manually, as well, which can be dependent on the weather and prone to human error.<h3>Safety Concerns</h3>Hull inspections come with inherent safety risks, especially when conducted by divers taking on tasks in complex environments. Divers may encounter low visibility, extreme temperatures, strong currents that can make maneuvering difficult and increase the chances of collision with objects or vessels, electrical hazards from equipment, and other dangerous or unpredictable conditions.There is also the potential for accidents, injuries, or health risks associated with prolonged exposure to underwater environments, such as decompression sickness and exposure to dangerous marine life.</section><section><h2>Enhancing Hull Inspections with ROVs</h2>ROVs are revolutionizing the field of hull inspection by offering a transformative approach to assessing the condition of a ship's exterior. These advanced underwater robots are equipped with high definition cameras, sensors, and manipulation tools, allowing them to navigate intricate spaces and gather accurate data without the need for direct human intervention. The integration of ROVs in hull inspections streamlines operations, enhances safety, and facilitates data-driven decision-making, paving the way for a more efficient and sustainable maritime industry.<h3>Target Identification With Sonar</h3><img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/6kjHANIlfEIo4qbGs7LdjK/b80d503170f1d68fa42d0c1fe46db5fc/MP1.png" alt="Advanced Data Capture" class="fr-fic fr-dii">By harnessing sonar capabilities, ROVs empower maritime professionals to navigate complex underwater environments, ensuring comprehensive and accurate data collection during hull inspections. This integration of ROVs with sonar technology can provide a clear perspective in murky, or low visibility conditions, visualize a ship or object from a distance, identify structural areas of interest, and can swiftly and precisely locate potential anomalies, such as cracks or corrosion, even in challenging underwater conditions.This advancement enhances the accuracy and efficiency of inspections by providing detailed real-time imagery and data, enabling maritime experts to make informed decisions about maintenance and repairs. The synergy of ROVs and sonar technology can expedite inspections, minimize downtime, and ultimately contribute to safer, more efficient, and cost-effective maritime operations.<h3>Mitigating Risks for Divers</h3>One of the greatest advantages of utilizing ROVs for hull inspections is the ability to minimize risks to human personnel. The integration of ROVs eliminates the necessity for divers to engage in perilous tasks like deep diving, operating in low visibility, unsafe surroundings, or enduring extreme temperatures. These underwater robots can access challenging or hazardous areas unsuitable for human divers, consequently diminishing the likelihood of accidents and injuries.ROVs also enable real-time monitoring of divers from topside supervisors, meticulous inspections, and data gathering, facilitating precise risk evaluations and well-informed decision-making. Collectively, the incorporation of ROVs into diving operations offers significant promise in enhancing dive safety and safeguarding the welfare of divers.</section><section><h3>Cost and Time Efficiency</h3>By replacing the need for manual, diver-based inspections, ROVs significantly reduce labor costs and operational expenses, don’t require extensive permits and paperwork, and can be deployed in a matter of minutes by a single operator. These underwater robots can also swiftly maneuver through complex structures, access hard-to-reach areas with ease, and conduct inspections in a fraction of the time it would take human divers.The real-time data capture capabilities of ROVs also accelerates the inspection process, enabling timely identification of potential issues. As a result, the integration of ROVs in hull inspection not only enhances accuracy and thoroughness but also delivers substantial savings in both time and resources, ultimately optimizing the overall efficiency of maritime maintenance practices.<h3>Recording Capabilities</h3>Equipped with high-definition cameras and sensors, ROVs facilitate detailed data capture and documentation during hull inspections. Recorded imagery and sensor data serve as comprehensive records, aiding in defect identification and tracking changes over time. This digital record enhances transparency, supports informed decision-making, and fosters effective communication among stakeholders, amplifying the precision and reliability of hull inspection processes.</section><section><h2>Hull Inspection Using Deep Trekker ROVs</h2><img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/4M0wQHlOHc0cx0eZllaYxZ/750e73f43ff09cfed1ac8a7b05e02841/deep_trekker_family_of_rovs.webp" alt="Family photo group glamour shot full res" class="fr-fic fr-dii">Deep Trekker leads the industry with innovative solutions designed to prioritize safety, efficiency, and customer satisfaction in hull inspections for the defense and security sector. Our ROVs are at the forefront of underwater inspection technology, offering portability, ease of operation, and affordability. Equipped with state-of-the-art features including 4K cameras and powerful LED floodlights, our ROVs redefine the standards for hull inspections, ensuring comprehensive assessments with unmatched precision and efficiency. Our commitment to excellence is reflected in every aspect of our products and services, guaranteeing unparalleled reliability and performanceTake a look at a couple of our robotic systems for hull inspections in the defense and security industry:<h3>REVOLUTION ROV</h3><img data-fr-image-pasted="true" src="https://downloads.ctfassets.net/kzewhs8e6cvu/4H4NjDluiIL0HzoHj9U7RF/0aaf965ab8e7fe2b67793651ffd272ac/REV_Standalone.png" alt="REVOLUTION at Pier" class="fr-fic fr-dii">The <a href="https://www.deeptrekker.com/products/underwater-rov/deep-trekker-revolution" target="_blank" rel="noopener">REVOLUTION</a> stands out as an exceptionally user-friendly and stable ROV for intricate underwater missions, powered by field-swappable lithium-ion batteries for extended portable usage. Driven by six powerful vectored thrusters, it offers unparalleled control and precise station holding capabilities. Additionally, it boasts an enhanced 4K camera for identifying smaller defects, featuring an expansive 260° field of view, a depth rating of 305 meters (1000 feet), and exceptionally bright LED floodlights.With tether options up to 500 meters (1,640 feet), the REVOLUTION ensures extensive operational range. It also harnesses the power of BRIDGE technology and effortlessly integrates with an extensive range of add-ons and sensors, including sonar and visualization systems for up-close inspections in murky conditions, stability and positioning systems, thickness gauge integration for structural inspections, and many more industry-specific tools, guaranteeing seamless mission execution.<h3>Expert Guidance for Your Projects</h3>Our team of seasoned industry professionals stands ready to offer <a href="https://www.deeptrekker.com/contact-us" target="_blank" rel="noopener">expert guidance and support</a> to seamlessly integrate Deep Trekker ROVs into your projects. From initial consultation to customized solutions tailored to your specific requirements, we are dedicated to ensuring the success of your underwater inspection endeavors. Contact us today to embark on your journey with Deep Trekker.</section>

Ship hull with biofouling during an inspection with a Deep Trekker ROV

Discover how ROVs revolutionize defense and security missions, ensuring vessel hull safety and integrity. Explore cutting-edge robotic technology.

ROVs for Defense and Security Hull Inspection

The humanoid robot driven by the robot AI world model unlocks many new skills!&nbsp;Strong power is waiting for you to develop!Breaking the full-size humanoid speed world record of 3.3m/s (the previous record was about 2.5m/s) Full body dynamic coordinated dance: Subject3&nbsp;Touch the height in place etc.&nbsp;More introduction: www.unitree.com/h1 <h2> </h2>

[full-size humanoid] Evolution V3.0

Unitree H1 Breaking humanoid robot speed world record

If someone advises you to “know your limits,” they’re likely suggesting you do things like exercise in moderation. To a robot, though, the motto represents learning constraints, or limitations of a specific task within the machine’s environment, to do chores safely and correctly.For instance, imagine asking a robot to clean your kitchen when it doesn’t understand the physics of its surroundings. How can the machine generate a practical multistep plan to ensure the room is spotless? Large language models (LLMs) can get them close, but if the model is only trained on text, it’s likely to miss out on key specifics about the robot’s physical constraints, like how far it can reach or whether there are nearby obstacles to avoid. Stick to LLMs alone, and you’re likely to end up cleaning pasta stains out of your floorboards.To guide robots in executing these open-ended tasks, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) used vision models to see what’s near the machine and model its constraints. The team’s strategy involves an LLM sketching up a plan that’s checked in a simulator to ensure it’s safe and realistic. If that sequence of actions is infeasible, the language model will generate a new plan, until it arrives at one that the robot can execute.This trial-and-error method, which the researchers call “Planning for Robots via Code for Continuous Constraint Satisfaction” (PRoC3S), tests long-horizon plans to ensure they satisfy all constraints, and enables a robot to perform such diverse tasks as writing individual letters, drawing a star, and sorting and placing blocks in different positions. In the future, PRoC3S could help robots complete more intricate chores in dynamic environments like houses, where they may be prompted to do a general chore composed of many steps (like “make me breakfast”).“LLMs and classical robotics systems like task and motion planners can’t execute these kinds of tasks on their own, but together, their synergy makes open-ended problem-solving possible,” says PhD student Nishanth Kumar SM ’24, co-lead author of a new paper about PRoC3S. “We’re creating a simulation on-the-fly of what’s around the robot and trying out many possible action plans. Vision models help us create a very realistic digital world that enables the robot to reason about feasible actions for each step of a long-horizon plan.”The team’s work was presented this past month in a paper shown at the Conference on Robot Learning (CoRL) in Munich, Germany.<iframe width="640" height="360" src="https://www.youtube.com/embed/hBq0EeFSsPo?&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>Teaching a robot its limits for open-ended chores. MIT CSAILThe researchers’ method uses an LLM pre-trained on text from across the internet. Before asking PRoC3S to do a task, the team provided their language model with a sample task (like drawing a square) that’s related to the target one (drawing a star). The sample task includes a description of the activity, a long-horizon plan, and relevant details about the robot’s environment.But how did these plans fare in practice? In simulations, PRoC3S successfully drew stars and letters eight out of 10 times each. It also could stack digital blocks in pyramids and lines, and place items with accuracy, like fruits on a plate. Across each of these digital demos, the CSAIL method completed the requested task more consistently than comparable approaches like <a href="https://arxiv.org/pdf/2403.11552">“LLM3”</a> and&nbsp;<a href="https://arxiv.org/pdf/2209.07753">“Code as Policies”</a>. The CSAIL engineers next brought their approach to the real world. Their method developed and executed plans on a robotic arm, teaching it to put blocks in straight lines. PRoC3S also enabled the machine to place blue and red blocks into matching bowls and move all objects near the center of a table.Kumar and co-lead author Aidan Curtis SM ’23, who’s also a PhD student working in CSAIL, say these findings indicate how an LLM can develop safer plans that humans can trust to work in practice. The researchers envision a home robot that can be given a more general request (like “bring me some chips”) and reliably figure out the specific steps needed to execute it. PRoC3S could help a robot test out plans in an identical digital environment to find a working course of action — and more importantly, bring you a tasty snack.For future work, the researchers aim to improve results using a more advanced physics simulator and to expand to more elaborate longer-horizon tasks via more scalable data-search techniques. Moreover, they plan to apply PRoC3S to mobile robots such as a quadruped for tasks that include walking and scanning surroundings.“Using foundation models like ChatGPT to control robot actions can lead to unsafe or incorrect behaviors due to hallucinations,” says The AI Institute researcher Eric Rosen, who isn’t involved in the research. “PRoC3S tackles this issue by leveraging foundation models for high-level task guidance, while employing AI techniques that explicitly reason about the world to ensure verifiably safe and correct actions. This combination of planning-based and data-driven approaches may be key to developing robots capable of understanding and reliably performing a broader range of tasks than currently possible.”Kumar and Curtis’ co-authors are also CSAIL affiliates: MIT undergraduate researcher Jing Cao and MIT Department of Electrical Engineering and Computer Science professors Leslie Pack Kaelbling and Tomás Lozano-Pérez. Their work was supported, in part, by the National Science Foundation, the Air Force Office of Scientific Research, the Office of Naval Research, the Army Research Office, MIT Quest for Intelligence, and The AI Institute.

PhD students Aidan Curtis (left) and Nishanth Kumar. To help robots execute open-ended tasks safely, the researchers used vision models to see what’s near the machine and model its constraints. Their “PRoC3S” strategy has an LLM sketch up an action plan that’s checked in a simulator to ensure it will work in the real world. Credits: Mike Grimmett/MIT CSAIL

The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.

Teaching a robot its limits, to complete open-ended tasks safely

RAVEN (Robotic Avian-inspired Vehicle for multiple ENvironments) © Alain Herzog CC BY SA

EPFL researchers have built a drone that can walk, hop, and jump into flight with the aid of birdlike legs, greatly expanding the range of potential environments accessible to unmanned aerial vehicles.

Bird-inspired drone can jump for take-off

Cleaning the seas of unexploded bombs, mines and other ordnance is essential but dangerous work. The farther that bomb disposal officers can stay away from explosive devices, the lower the risk they face. This is why the US Navy and other military forces prefer to put underwater remotely operated vehicles (ROVs) in harm’s way. And the chosen vehicle for many is supplied by US-based VideoRay, manufacturer of the Ally, Defender and Pro 5 ROVs. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733910037880-Videoray+customer+case_Figure+1.png" style="width: 100%px;" class="fr-fic fr-dib"> VideoRay’s products have gained support from the demanding military community because of their combination of small size and portability, high power and lifting ability, flexible payload options, and reliability. And playing an important part in the stability system of the VideoRay ROVs, making remote operation of the vehicle, its video camera and other accessories easier, is the high performance of the <a href="https://www.movella.com/products/sensor-modules" target="_blank" rel="noopener noreferrer">Xsens MTi</a> Attitude and Heading Reference System (AHRS) sensor on board.&nbsp;A robotic diver with high-resolution camera visionThe Ally, Defender and Pro 5 ROVs operate like a diver, but without putting a human life at risk. &nbsp;Built to a modular design and tethered to a control panel on the surface, the ROV has a 4K camera, optional auxiliary cameras &nbsp;and powerful lights to enable the remote operator to view the scene in detail, and forward-looking sonar to assist with piloting the vehicle in murky water and other low visibility environments. &nbsp;VideoRay offers a wide range of accessory options for equipping the vehicle with gripping, lifting and other tools. And with seven 1hp thrusters on board, the ROV can move fast through the water and lift heavy objects with ease from the sea floor.&nbsp; <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733910100730-Videoray+customer+case_Figure+2.png" style="width: 100%px;" class="fr-fic fr-dib"> Since the ROV is tethered, it can be remotely piloted. The VideoRay products are widely used by the US Navy on explosive ordnance disposal missions. Here, the ROV navigates semi-autonomously to a zone of interest, heading for destination co-ordinates programmed in by the operator. Once the ROV reaches the zone of interest, a remote pilot takes over to locate the explosive device, and perform the mission’s operation on it. Inertial navigation technology keeps the ROV on track – a combination of a DVL (Doppler velocity log), GPS satellite positioning, Greensea internal navigation software, a pressure sensor for depth measurement, and the Xsens MTi AHRS sensor for tracking the vehicle’s heading. The attitude readings from the sensor also support the operation of the propulsion system, enabling the vehicle to maintain a stable posture underwater.&nbsp;Accurate heading keeps the ROV on trackAndy Goldstein, Chief Technology Officer of VideoRay, explains that the heading measurements are normally the biggest source of error in marine inertial navigation systems. This means that the accuracy of the AHRS has a substantial impact on the ability of the ROV to reliably reach the destination programmed into it. He says, ‘VideoRay’s evaluation of the Xsens MTi AHRS sensor convinced us that its performance would meet the high standards that we set for the inertial system on the ROV. Movella supplies the sensors with a robust software development kit to streamline the system development process. The SDK is also supported by magnetic calibration software supplied by Movella. This means that users can calibrate the sensor’s heading measurements to adjust for variations in the earth’s magnetic field. This fine tuning of the heading system’s outputs is absolutely crucial to the vehicle’s ability to stay on track over missions stretching for hundreds of meters from the starting position.’The other key factors in VideoRay’s decision to use Xsens MTi AHRS sensors are its size and availability in production volumes. Its small size made it easier for the VideoRay engineering team to fit the sensor inside the compact ROVs, which can be carried by a single person. &nbsp;&nbsp; <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733910118532-Videoray+customer+case_Figure+3.png" style="width: 100%px;" class="fr-fic fr-dib"> The powerful propulsion and reliable autonomous navigation capabilities of the <a href="https://videoray.com/" target="_blank" rel="noopener noreferrer">VideoRay</a> ROVs have been tested extensively in thousands of explosive ordnance disposal missions operated by the US Navy and others. The ROVs are also in use in many applications which require their power, portability and flexibility: offshore energy mapping and production, ship maintenance, inspection of underwater assets such as dams, search and rescue and others.&nbsp;And in all of them, the inertial navigation and handling of the vehicle are assisted in large part by the high-performance Xsens MTi AHRS sensor on board.&nbsp;

With Xsens MTi AHRS

VideoRay underwater ROVs on board provide reliable autonomous navigation for hazardous US Navy missions

Professional piano players spend countless hours at the keys, perfecting their craft. For people with smaller hands, this dedication can take a physical toll. Repeated stretching to reach distant keys in a chord can strain muscles and joints and may lead to tendonitis, carpal tunnel syndrome, and other injuries.Researchers at Stanford are working to understand the forces involved in the hand movements of elite-level piano players, and how those change with different hand sizes and keyboard widths. On Dec. 5 at SIGGRAPH Asia 2024, Stanford Engineering researchers presented an AI-trained model that can recreate the hand movements required to play complicated pieces of music and the physical stresses those hands are under. This is the first step in an effort to reduce the risk of long-term injuries in piano players and test solutions – such as narrower keyboards – that could make piano playing more equitable for everyone.“We would never expect a world-class athlete to compete with equipment that does not fit their body. Yet we ask pianists, particularly women, to adapt to a one-size-fits-all design that was never built with them in mind,” said<a href="https://profiles.stanford.edu/elizabeth-schumann" data-extlink="">&nbsp;Elizabeth Schumann</a>, the Billie Bennet Achilles Director of Keyboard Studies at the&nbsp;<a href="https://humsci.stanford.edu/" data-extlink="">School of Humanities and Sciences</a> and co-author on the paper. “We can use research like this to reshape the future of performance and make it more sustainable.”&nbsp;<h2>Capturing elite performance</h2>The modern piano keyboard was designed in the 19th century with the average European man in mind. Today, an estimated 87% of adult women and 24% of adult men have hands that are smaller than ideal for the standard piano. Using traditional methods to study the impacts of smaller hands would require following a cohort of piano players for decades, and it could still be difficult to quantify injury risks or test solutions. Instead, the researchers have recorded the hand movements of professional piano players and are using artificial intelligence to predict how different sizes of hands would move to play new music.Schumann and her colleagues recruited 15 elite-level pianists to play a total of 10 hours of music while cameras filmed their hands from every angle. The researchers couldn’t put any sensors on the players without potentially interfering with their performance, so they used advanced computer vision techniques to combine the videos and reconstruct the players’ hand motions in three dimensions. They used additional processing to make sure that those movements were perfectly synced with the audio.“The quality of data that we were able to achieve is unprecedented,” said&nbsp;<a href="https://profiles.stanford.edu/c-karen-liu" data-extlink="">Karen Liu</a>, a professor of computer science at Stanford and lead author on the paper. “It’s not just that the reconstructed 3D motions are accurate and clean; the quality of the performance itself is truly remarkable. We were able to capture diverse movements from a group of talented musicians performing at a high level – I have not seen a dataset like that in the past.”Roucheng Wang, a graduate student in Liu’s lab, and Pei Xu, a postdoctoral researcher, used this dataset to train a model to accurately generate new piano-playing data. When they gave the model sheet music it had never seen, such as Beethoven’s “Für Elise,” the model could provide the correct 3D hand movements needed to play the piece, using a simulated piano to generate sound.“I was just so stunned at how accurately this model could simulate elite-level technique,” Schumann said. “It’s really incredible.”<iframe width="640" height="360" src="https://www.youtube.com/embed/sGQNCEl7pJQ?&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>When researchers gave the AI-trained piano playing model sheet music it had never seen, such as Beethoven’s “Für Elise,” the model could provide the correct 3D hand movements needed to play the piece. | Ruocheng Wang<h2 id="isPasted">From robot joints to real muscles</h2>This model is just the first step for Liu, Schumann, and their colleagues. At the moment, the model can physically simulate hand movements, but it doesn’t simulate the muscles and tendons that create those movements or the strain that they are subjected to.“We are not at the biomechanics level yet, where the actuation of the hands is generated by muscles – currently our model is more like a robot that directly generates torque at joints,” Liu said. “Eventually, our model needs to be able to predict the tension level of muscles and the potential of injury.”The researchers are currently working to add these layers to their model so that they can use it to help piano players – especially those with small hands – avoid injuries. They are also adapting their work to model the movements of other dexterous musicians. They have applied some of the same techniques to an existing dataset of hand motions of highly skilled guitar players. Their results, which were also presented recently at SIGGRAPH Asia 2024, showed that they could successfully model the distinct left- and right-hand movements needed to play a variety of both popular and classical music.“With high-quality data, we could model the 3D movements needed for other types of music performance as well,” Liu said. “This isn’t a model that is replacing people – we’re working with musicians to help understand and solve problems. This project is about advancing people, and AI is just a tool for that.”

Stanford researchers collected the first large-scale 3D hand motion dataset containing 10 hours and 153 pieces of piano music performed by 15 elite-level pianists, along with synchronized audio and key pressing events. | The Movement Lab

Researchers at Stanford Engineering have developed an AI-trained model to accurately recreate the hand movements of elite-level pianists and the physical stresses they endure while playing.

AI could help reduce injury risk in pianists

According to <a href="https://www.statista.com/outlook/tmo/artificial-intelligence/ai-robotics/worldwide" target="_blank" id="isPasted">Statista</a>, the global AI robotics market will reach $17 billion in 2024 and will keep growing by 24% annually. Today, robots deliver packages, conduct geological surveys, and entertain visitors of parks and exhibitions – however, that’s not the limit of their abilities. In this article, we get into the technologies at the intersection of AI and robotics, as well as the research done in this field at ITMO, with Prof. Ivan Borisov, a researcher at ITMO’s International Laboratory of Biomechatronics and Energy-Efficient Robotics (BE2R Lab).<h1 dir="ltr" id="isPasted">Why mix AI and robotics</h1>Robotics is an interdisciplinary field that brings together mechanics, electronics, and control systems. In order to plan a robot’s route and manage its movements, experts need to have a good command of mathematics and algorithms. Without correctly functioning optimization algorithms, a robot won’t be able to plan its movement trajectory and steps, especially if there are obstacles in its way.A control algorithm is based on an accurate mathematical model of a robot, which describes its state, geometry, and dynamics. Using this model, the algorithm calculates the robot’s movements and interactions with the environment. In other words, the more precise the mathematical model is, the more efficient the actual robot will be.To build an accurate mathematical model, it is essential to consider key geometric parameters (such as mass, inertia, friction, drive power, torque, as well as the performance metrics of drives and sensors). For a robot isolated from its environment, creating an accurate model is straightforward. However, the task becomes fundamentally more complex when the robot is required to physically interact with objects in its environment, especially when this environment is unknown.<blockquote>“We can, for example, build an accurate model of a manipulator robot welding a car. It has a fixed base and predetermined environment – we know what will happen to it. That’s why we can make accurate models of the robot itself and its surroundings, calculate the welding machine’s trajectory, and make the robot simply follow a precalculated trajectory. However, if we need a mobile robot, one that will walk or move on wheels in a city, or a kitchen manipulator robot, the conventional paradigm would require a model of its surroundings. The case is, though, that we cannot create an accurate model for an undetermined, a priori unknown, and constantly changing environment,” says Prof.&nbsp;Ivan Borisov, a researcher at ITMO’s International Laboratory of Biomechatronics and Energy-Efficient Robotics (BE2R Lab).</blockquote><iframe width="640" height="360" src="https://www.youtube.com/embed/TbyHKQKYVHg?&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"></iframe>AI technologies can be used to address the challenges of designing robots as systems. For this purpose, researchers turn to gradient and global optimization algorithms, as well as neural network approaches, including training on large datasets and reinforcement learning. Thanks to these methods, it’s possible to automate the design process (by delegating the filtering of solutions to algorithms), solve the task of motion policy synthesis, and enable robots to perceive their environment.<h1 dir="ltr">How AI is facilitating robot design</h1>One robotics task that AI is already helping to solve is the design of entire robots or their individual parts, such as mechanisms or drives.Previously, the quality of a design depended solely on the skills of its creator: if the same task was given to several engineers, they would all solve it differently. As a result, there would be various designs, each with hardly guaranteed optimality.These days, this problem is being addressed through the use of numerical and generative design. The main goal is to automate the design process and help specialists find optimal robot designs more quickly by exploring all possible solutions. Numerical design can be based on both optimization algorithms and neural network approaches, such as reinforcement learning or supervised learning. It helps identify the optimal parameters to enhance a robot’s performance, while generative design helps create mechanical structures and optimize them.Reinforcement learning was proven effective in the industry. For example, it was used by Disney Research in collaboration with ETH Zurich to <a href="https://la.disneyresearch.com/publication/design-and-control-of-a-bipedal-robotic-character/" target="_blank">merge</a> the animation of a droid from Star Wars and a physical model of a bipedal robot.&nbsp;Researchers from the BE2R Lab have <a href="https://news.itmo.ru/en/science/cyberphysics/news/13989/" target="_blank">created</a> the open-source repository <a href="https://github.com/aimclub/rostok?tab=readme-ov-file" target="_blank">Rostok</a>; with it, it’s possible to use generative design to create single-drive adaptive grabbing tools. Taking into account the parameters given by an engineer and the robot’s design limitations, the technology balances between them, presenting a greater number of various solutions. This makes the design process quicker (compared to human performance) and mathematically justified. Each solution is tested in a simulator and graded on performance. The higher the grade, the more likely the algorithm is to recommend a particular solution to engineers.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733879540331-1399600.jpg" style="width: 100%px;" class="fr-fic fr-dib">Grabbing robot design. Photo courtesy of ITMO scientistsThe researchers used a similar approach to improve their previous <a href="https://news.itmo.ru/en/news/13232/" target="_blank">design</a> – a mobile jumping robot. Earlier, the robot relied on bearings, making its structure more rigid and less mobile; during a fall, part of the impact energy damaged its solid links. Using global optimization genetic algorithms, the scientists <a href="https://news.itmo.ru/en/science/cyberphysics/news/13925/" target="_blank">found out</a> that it would be better to use soft links instead of bearings. By absorbing all the energy from a fall, such links protect the robot’s structure and allow for partial recouping of energy from the impact. Thanks to this change, quadrupeds, anthropomorphic robots, and collaborative manipulators can be more energy-efficient, withstand shock loads, and adapt to uneven surfaces.&nbsp;Both solutions were revealed at the International Conference on Intelligent Robots and Systems (IROS 2024), a major conference that <a href="https://portal.core.edu.au/conf-ranks/?search=IROS&by=all&source=all&sort=dsource&page=1" target="_blank">holds the A rank</a> (one of the highest) in the CORE conference ranking.. In 2024, the event was held in Abu Dhabi on October 14-18.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733879566211-1399601.jpg" style="width: 100%px;" class="fr-fic fr-dib">Jumping robot prototype. Photo courtesy of ITMO scientists<h1 dir="ltr" id="isPasted">AI for robotic vision</h1>Computer vision is a key element in robotic design, as it helps robots build a 3D map of their surroundings and plan routes around obstacles. However, without AI solutions to these tasks are too expensive, time-consuming, and inefficient.In the 1980s, Prof. Hans Moravec from Carnegie Mellon University explained why vision became one of the hardest “capacities” to acquire for robots. It’s summarized in a paradox subsequently named after him: contrary to popular assumption, higher cognitive processes (such as playing chess or go) require relatively little computational resources and are thus available to robots; at the same time lower-level sensorimotor operations (vision, perception, and body coordination) require immense computational resources.&nbsp;With AI, it’s possible to overcome Moravec’s paradox. For that, engineers first assemble a dataset (or take an existing one) wherein images are clearly marked by features and categories: for example, some are cats, and some are humans. To map out its surroundings and plan routes, robots use LiDARs, cameras, and other sensory technologies, while trained networks help them “make sense” of the surrounding objects. If a robot encounters something unknown along the way, it might try to classify this object using similar images from its dataset.AI can also help robots navigate complex environments with mirrors or glass doors. Their surfaces reflect or permit light through, which means that robots can misinterpret obstacles or distances to them.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733879591092-1399604.jpg" style="width: 100%px;" class="fr-fic fr-dib">Sergey Kolyubin with the team of ITMO's Faculty of Control Systems and Robotics. Photo by ITMO.NEWSHeaded by Sergey Kolyubin, researchers from ITMO’s BE2R Lab have&nbsp;<a href="https://ieeexplore.ieee.org/abstract/document/10342329" target="_blank" id="isPasted">developed</a> a 3D mapping system based on deep neural networks and the camera’s ego-motion (its movement relative to surrounding objects). Having tested the system in a space with glass partitions, the researchers concluded that it calculates distance to objects and creates 3D maps of spaces more accurately than other neural network-based solutions with RGB-D cameras that calculate distances to objects and their colors.

According to , the global AI robotics market will reach $17 billion in 2024 and will keep growing by 24% annually. Today, robots deliver packages, conduct geological surveys, and entertain visitors of parks and exhibitions – however, that’s not the limit of their abilities.

Higher, Faster, Stronger: How AI Makes Robots Smarter and More Efficient

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

A tiny, four-fingered “hand” folded from a single piece of DNA can pick up the virus that causes COVID-19 for highly sensitive rapid detection and can even block viral particles from entering cells to infect them, University of Illinois Urbana-Champaign researchers report. Dubbed the NanoGripper, the nanorobotic hand also could be programmed to interact with other viruses or to recognize cell surface markers for targeted drug delivery, such as for cancer treatment.&nbsp;Led by <a href="https://bioengineering.illinois.edu/people/xingw">Xing Wang</a>, a professor of <a href="https://bioengineering.illinois.edu/">bioengineering</a> and of <a href="https://chemistry.illinois.edu/">chemistry</a> at the U. of I., the researchers describe their advance in the journal <a href="https://www.science.org/doi/10.1126/scirobotics.adi2084">Science Robotics</a>.&nbsp;Inspired by the gripping power of the human hand and bird claws, the researchers designed the NanoGripper with four bendable fingers and a palm, all in one nanostructure folded from a single piece of DNA. Each finger has three joints, like a human finger, and the angle and degree of bending are determined by the design on the DNA scaffold.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733792836250-232743.jpg" style="width: 100%px;" class="fr-fic fr-dib">Inspired by the human hand or bird claws, the NanoGripper has four fingers and a palm, all folded from one piece of DNA. Image courtesy of Xing Wang“We wanted to make a soft material, nanoscale robot with grabbing functions that never have been seen before, to interact with cells, viruses and other molecules for biomedical applications,” Wang said. “We are using DNA for its structural properties. It is strong, flexible and programmable. Yet even in the DNA origami field, this is novel in terms of the design principle. We fold one long strand of DNA back and forth to make all of the elements, both the static and moving pieces, in one step.”The fingers contain regions called DNA aptamers that are specially programmed to bind to molecular targets — the spike protein of the virus that causes COVID-19, for this first application ­— and trigger the fingers to bend to wrap around the target. On the opposite side, where the wrist would be, the NanoGripper can attach to a surface or other larger complex for biomedical applications such as sensing or drug delivery.&nbsp;To create a sensor to detect the COVID-19 virus, Wang’s team partnered with a group led by Illinois electrical and computer engineering professor <a href="https://ece.illinois.edu/about/directory/faculty/bcunning">Brian Cunningham</a>, who specializes in biosensing. They coupled the NanoGripper with a photonic crystal sensor platform to create a rapid, 30-minute COVID-19 test matching the sensitivity of the gold-standard qPCR molecular tests used by hospitals, which are more accurate than at-home tests but take much longer. &nbsp;&nbsp;“Our test is very fast and simple since we detect the intact virus directly,” Cunningham said. “When the virus is held in the NanoGripper’s hand, a fluorescent molecule is triggered to release light when illuminated by an LED or laser. When a large number of fluorescent molecules are concentrated upon a single virus, it becomes bright enough in our detection system to count each virus individually.”&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733792872784-232744.jpg" style="width: 100%px;" class="fr-fic fr-dib">The NanoGripper can be designed to detect a number of viruses or other molecules, or even for targeted drug delivery for cancer treatment, the researchers say. Pictured, from left: researcher Wei Hong, graduate student Linh Le, postdoctoral researcher Mengxi Zheng, Professor Xing Wang, Professor Brian Cunningham, graduate student Skye Shepherd, research fellow Tingjie Song and postdoctoral researcher Saurabh Umrao.In addition to diagnostics, the NanoGripper could have applications in preventive medicine by blocking viruses from entering and infecting cells, Wang said. The researchers found that when NanoGrippers were added to cell cultures that were then exposed to COVID-19, multiple grippers would wrap around the outside of the viruses. This blocked the viral spike proteins from interacting with receptors on the cells’ surface, preventing infection.&nbsp;“It would be very difficult to apply it after a person is infected, but there’s a way we could use it as a preventive therapeutic,” Wang said. “We could make an anti-viral nasal spray compound. The nose is the hot spot for respiratory viruses, like COVID or influenza. A nasal spray with the NanoGripper could prevent inhaled viruses from interacting with the cells in the nose.”&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733792895626-232746.png" style="width: 100%px;" class="fr-fic fr-dib">An artistic rendering of the NanoGripper’s applications. Sites on the gripper’s fingers recognize the spike protein of a virus, inset, and trigger fluorescent tags to emit light. When coupled with a sensor, individual viruses can be detected for a rapid COVID test, foreground. Alternately, the NanoGrippers can block viruses from entering cells by wrapping around the spike proteins, top right.The NanoGripper could easily be engineered to target other viruses, such as influenza, HIV or hepatitis B, Wang said. In addition, Wang envisions using the NaoGripper for targeted drug delivery. For example, the fingers could be programmed to identify specific cancer markers, and grippers could carry cancer-fighting treatments directly to the target cells.“This approach has bigger potential than the few examples we demonstrated in this work,” Wang said. “There are some adjustments we would have to make with the 3D structure, the stability and the targeting aptamers or nanobodies, but we’ve developed several techniques to do this in the lab. Of course it would require a lot of testing, but the potential applications for cancer treatment and the sensitivity achieved for diagnostic applications showcase the power of soft nanorobotics.” &nbsp;<hr>The National Institutes of Health and the National Science Foundation supported this work. Wang and Cunningham are affiliated with the <a href="https://www.igb.illinois.edu/">Carl R. Woese Institute for Genomic Biology</a> and the&nbsp;<a href="https://hmntl.illinois.edu/">Holonyak Micro and Nanotechnology Lab</a> at the U. of I.&nbsp;

Illinois researchers developed a nanorobotic hand made of DNA that can grab viruses for detection or inhibition. In this artist’s rendering, three “NanoGripper” hands wrap around a COVID-19 virus. Image courtesy of Xing Wang

A tiny, four-fingered “hand” folded from a single piece of DNA can pick up the virus that causes COVID-19 for highly sensitive rapid detection and can even block viral particles from entering cells to infect them, University of Illinois Urbana-Champaign researchers report.

Nanorobot hand made of DNA grabs viruses for diagnostics and blocks cell entry

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Unitree B2 has evolved once again, is there an end?

<h2 dir="ltr" id="isPasted">Introduction</h2>Industrial machinery, from robots to crane booms, require sensing and guidance to ensure their effective use. Beyond the industrial world, the latest video cameras detect and compensate for movement and vibration to create high-quality imagery. In fact, accurate sensing of orientation and movement is integral to everything from communication satellites to farming equipment.Inertial measurement units (IMUs) allow the precise measurement of motion and orientation. When combined with accurate positioning information provided by global navigation satellite systems (GNSS), IMUs can provide location and velocity information, even when satellite signals are weak or obstructed.Selecting the right IMU should be easy. Manufacturer’s data sheets provide access to all the information an engineer needs to choose the perfect component for their application. And yet, engineers are designing inaccuracy into their equipment as they are unable to gain a full understanding of how IMUs perform from data sheets alone.In this paper, we will highlight three key challenges that engineers face when specifying an IMU for their next design. We will help them to read between the lines of the data sheet to understand the information they need. We will look at how manufacturers test their products, and we will explain how the environment can affect the performance of IMUs.<iframe width="540" height="370" src="https://c8056756.sibforms.com/serve/MUIFAFzOYRmakTTHP2FX2FyHP3QxXkV_prDOkSjxVq9r73O2ZYgeTBdxzjryZ1uiV-lwmna05gucMW0bsczCM_L1y82QMtCTmxYMAjh47rKxZQYNnUMylX2XNlQ7TpTNyLxACx2rLDFoQXvwdl20h6UYtI005O05w77j-WIyUR8ngfSBpL3p7bGQ8Cu-7pjUFhSkY3xZfKxVGSlQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable"></iframe> <h2 dir="ltr">Chapter 1: Getting the Whole Picture</h2>Engineers selecting the components for a new design will rely upon the manufacturer's information. The wealth of information provided by data sheets and catalogues allows the engineer to make decisions based on the component’s predicted performance. The key attributes that must be understood when selecting IMUs are bias instability and angle random walk (ARW). Bias instability is caused by tiny fluctuations in an IMU’s zero-bias level. This is the reading that the sensor delivers when it is at rest, and therefore provides the baseline for an IMU that is stationary. IMUs are affected by a range of external conditions, including variations in temperature and vibration, which reduce the accuracy and stability of the IMU's output. This change is characterized by a slow, long-term drift and is measured in degrees of drift per second (deg/s).In contrast to bias instability, angle random walk is caused by random high-frequency noise inside the IMU which causes variations in the measurement of angular velocity over time. The errors that ARW tries to quantify grow with the square root of time, and so the accumulated angle error will grow over a long period.Both attributes are, therefore, heavily influenced by time, a problem that is made worse by the dynamic conditions that IMUs are intended to measure. However, the data that is typically provided in IMU data sheets does not reflect this dynamic nature, and both bias instability and ARW are often published as a single number. This reduction of a complex system to a single number does not provide the engineer with a clear prediction of its performance over time. By relying on single attributes, engineers run the risk of designing inaccuracies into their device before it is even used.Instead of relying on the single value of the gyro bias instability and ARW, engineers can turn to Allan deviation plots. An Allan deviation plot (or Allan variance plot) is a graphical tool that provides a more useful understanding of the stability and noise characteristics of IMUs. The plot is unique to each IMU and helps identify the effects of different types of noise by showing how the variance of a sensor’s output changes over different averaging times. This is especially useful for assessing the drift and noise behaviour over time.This time dependency is the key advantage of using Allan deviation instead of traditional standard deviation. Standard deviation is a useful tool if the mean of the data is expected to be constant, and the measurements do not drift over time. This is not the case with IMUs. An Allan plot is derived from data collected from the IMU over a period of time, calculated at an interval known as tau (𝜏). This represents the averaging time, and the higher the tau, the more that individual extreme readings will be averaged out.The image below shows a typical Allan deviation plot for an IMU, in which the low point corresponds to the bias instability, whereas the value corresponding to tau of 1 sec (1 second averaging) represents the angular random walk, reflecting the higher frequency fluctuations that define ARW.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733673242419-banner+epson.jpg" style="width: 100%px;" class="fr-fic fr-dib">Another data point concerns warm-up time. Extreme temperatures can affect the warm-up time of the IMU. This is the period after activation that the IMU needs to reach its operational state. In just one example, after hours exposed to sub-zero temperatures, the warm-up time for an IMU may be considerably longer than published to allow the internal components to stabilize before they can provide accurate readings.To avoid designing undesired inaccuracy into a new device, it is therefore vital for the engineers to access the best possible data. The preferred path for the engineer is to work with an IMU manufacturer that can provide this comprehensive information, for the alternative is for the engineer to conduct their own evaluation, a time-consuming process.While the information presented in data sheets is important, the engineer will only gain a real insight into the performance of their chosen component by understanding the underlying data and how it will affect their application in the long run.<h2 dir="ltr">Chapter 2: IMUs in the Real World</h2>Accurate and comprehensive information is therefore vital, but it is important for the engineer to understand the conditions under which it was obtained. Systems and equipment that use IMUs are regularly deployed beyond the safe environment of the laboratory into the real world. The conditions they encounter can have a significant effect on the performance of IMUs.An example of these tough conditions can be found in modern farming. The agricultural industry is embracing the latest sensing technology, collecting data to maximize efficiency. &nbsp;This data is used to make decisions, from identifying optimum soil conditions to deploying the right resources.&nbsp;Accurate positioning is essential for the smart farm. The ability to precisely control planting, fertilizing and harvesting activities allows the farmer to improve efficiency, yields, and sustainability. The huge growth in the development of autonomous tractors, harvesters, and drones places even more emphasis on navigation and positioning. These systems make use of global navigation satellite systems (GNSS) to allow these machines to operate within centimeters of their intended paths, without the need for human intervention.However, GNSS signals may not always be available as satellite signals can become disrupted or blocked. Atmospheric conditions, interference from structures and geographical features, along with the orbital geometry of the satellites themselves can all cause loss of signals. Without satellite guidance, autonomous machinery cannot obtain the accurate position that is so important to precision farming.Under these circumstances, the IMU becomes the principal means by which the equipment can locate their position. Using a process of dead reckoning, the IMU can measure the movement from the last known position provided by GNSS until the signal can be reacquired. The same method can be employed by a huge range of industries far from the smart farm, from the delivery of medical supplies by drone into remote locations to the automated loading of ships at ports.This reliance on IMUs in the real world means that they must withstand a range of conditions. Some of the most important effects to consider are those caused by shock and vibration. IMUs can be affected by both high and low frequencies. Some frequencies can cause an effect called sensor saturation in which the IMU can no longer work correctly as the vibration exceeds the IMU’s limiting value. Engineers must also be aware of the physical damage that vibration and sudden shock can cause to equipment.Engineers must also understand the effect of extreme temperatures on IMUs. In the earlier example, we looked at the need for accurate positioning on the smart farm. The agricultural environment is one of the harshest in the world, and vehicles are designed to function in an extraordinary range of conditions. Whether deployed to the frozen tundra in the Arctic circle or the unforgiving heat of deserts, equipment is exposed to ambient temperatures from -40°C to near the boiling point of water.This range of temperatures has a significant impact on the accuracy and performance of IMUs. Bias instability changes with temperature, which can lead to cumulative errors, especially for measurements based on integration over time. To complicate matters further, the drift in bias caused by changes in temperature are often nonlinear. This means that the designer needs to calibrate their system over the expected operating temperature range to ensure accuracy.Engineers must therefore understand the environment into which their equipment will be deployed. Identifying harsh conditions on the road or in the factory will allow engineer to choose the correct IMU based on accurate data. This may require information obtained from the IMU manufacture that is not contained in the data sheet. It is essential to understand how temperature will affect warm-up time, and to request a calibration profile that details temperature-dependent drifts.<h2 dir="ltr">Chapter 3: Consistency is the Key</h2>While it is important for the engineer to understand the conditions to which their design will be exposed, it is also crucial to know how the data provided by the manufacturer was created. This has important implications for consistency. Data sheets for IMUs often present typical performance values, without providing clear information about how this information was obtained. In many cases, the published information is an average, but the data sheet will rarely provide details about the statistical methodology used to obtain the average. Without the all-important description of this typical performance, the engineer can only guess how the IMU will perform in their specific application.When evaluating IMUs, it is important to understand the level of statistical confidence behind the performance data. Where some manufacturers will publish performance at a 1-sigma level, others will use 2-sigma. The statistical difference between different sigma levels has important implications for high-performance applications in which consistency is key.Sigma levels are use used in statistics to describe a confidence level in the data provided. In a normal distribution of data, a 1-sigma (1σ) level represents one standard deviation away from the mean. At this level, roughly two-thirds (in fact, 68.27%) of the data values will fall within this one standard deviation from the mean. In real terms, it means that there is a two-thirds probability that any single measurement will fall within this range.However, if a manufacturer bases their data on a 2-sigma (2σ) level, this probability rises to more than 95%. As a result, any one measurement will fall within the predicted range 19 times out of 20, delivering a far higher level of confidence in the data provided. In the context of IMUs, this confidence translates into an expectation of accuracy over time.This variation in predicted consistency is further complicated by the testing regime employed by the manufacturer. Some manufacturers adopt a strategy of batch testing, evaluating only a small number of units to provide the data required. Other manufacturers may conduct multiple tests on a single unit in the expectation that this will provide an accurate prediction of the performance of the entire range. The result is that the listed performance may not accurately reflect the variability between different IMUs from the same production batch. This variability can be significant, particularly for critical characteristics like bias instability.Unfortunately, the only way to truly understand how an IMU will perform in a real-world application is for the engineer to perform their own evaluation. While data sheets can provide a baseline, real-world testing is necessary to understand the variability between units and ensure consistent performance in the field. Working with a trusted manufacturer that publishes clear and comprehensive information can go a long way to helping the engineer with their understanding.<iframe width="540" height="370" src="https://c8056756.sibforms.com/serve/MUIFAFzOYRmakTTHP2FX2FyHP3QxXkV_prDOkSjxVq9r73O2ZYgeTBdxzjryZ1uiV-lwmna05gucMW0bsczCM_L1y82QMtCTmxYMAjh47rKxZQYNnUMylX2XNlQ7TpTNyLxACx2rLDFoQXvwdl20h6UYtI005O05w77j-WIyUR8ngfSBpL3p7bGQ8Cu-7pjUFhSkY3xZfKxVGSlQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable"></iframe> <h2 dir="ltr">Conclusion</h2>Selecting the right inertial measurement unit is a complex task. Not only are IMUs required to accurately measure movement, but they are expected to deliver high reliability in some of the most demanding conditions..Engineers need to understand how these conditions will affect the performance of their IMU. The roles played by extremes of temperature and the physical demands of dynamic applications must be considered before the engineer can have confidence on their design. Unfortunately, some manufacturers do not provide easy access to the detailed information that engineers need. Therefore, when selecting an IMU, here are the key considerations for engineers:<ul><li dir="ltr">Remember that the data sheet might not contain the depth of information required. Do not rely on single-number attributes, and obtain Allan deviation plots to gain a full understanding of the device’s performance.</li><li dir="ltr">Device testing takes place in the controlled environment of the laboratory, but the real-world is not so accommodating. Extremes of temperature and physical demands play a huge role in the reliability and accuracy of IMUs. Engineers should ensure that they understand their environment.</li><li dir="ltr">Take the time to understand how IMUs are tested. Confidence in performance comes from extensive testing methods, and clearly published data from the manufacturer. If necessary, engineers should be ready to conduct their own evaluation.</li></ul><a href="https://global.epson.com/products_and_drivers/sensing_system/imu/">Epson</a> manufactures a comprehensive range of IMUs for a huge array of applications, from earthquake detection to the precise positioning of satellites in space. Epson creates highly capable sensing systems that offer outstanding precision, high stability, and low noise to deliver confidence to design engineers in the most demanding industries.

Understanding IMU data sheets, manufacturers' testing methods, and the impact of environmental factors on performance.

Three Key Considerations for Selecting IMUs

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What is a Cobot? Advanced Guide to Collaborative Robot Architecture and Implementation

Cornell researchers in physics and engineering have created the smallest walking robot yet. Its mission: to be tiny enough to interact with waves of visible light and still move independently, so that it can maneuver to specific locations – in a tissue sample, for instance – to take images and measure forces at the scale of some of the body’s smallest structures.“A walking robot that’s small enough to interact with and shape light effectively takes a microscope’s lens and puts it directly into the microworld,” said <a href="https://physics.cornell.edu/paul-mceuen">Paul McEuen</a>, the John A. Newman Professor of Physical Science Emeritus in the College of Arts and Sciences (A&amp;S), who led the team. “It can perform up-close imaging in ways that a regular microscope never could.”The team’s paper, “<a href="https://www.science.org/doi/10.1126/science.adr2177">Magnetically Programmed Diffractive Robotics</a>,” published Nov. 28 in Science, with McEuen as corresponding author. <a href="https://www.lassp.cornell.edu/people/conrad-smart">Conrad Smart</a>, researcher at Cornell’s Laboratory of Atomic and Solid State Physics (LASSP), and Tanner Pearson, Ph.D. ’22, are the study’s co-first authors.Cornell scientists already hold the world’s record for the <a href="https://news.cornell.edu/stories/2020/08/laser-jolts-microscopic-electronic-robots-motion">world’s smallest walking robot at 40-70 microns</a>.The new diffractive robots are “going to blow that record out of the water,” said <a href="https://physics.cornell.edu/itai-cohen">Itai Cohen</a>, professor of physics (A&amp;S) and a co-author of the study. “These robots are 5 microns to 2 microns. They’re tiny. And we can get them to do whatever we want by controlling the magnetic fields driving their motions.”Diffractive robotics connects, for the first time, untethered robots with imaging techniques that depend on visible light diffraction – the bending of a light wave when it passes through an opening or around something. The imaging technique requires an opening of a size comparable to the light’s wavelength. For the optics to work, the robots must be on that scale, and for the robots to reach targets to image, they have to be able to move on their own. The Cornell team has achieved both objectives.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733532207601-1202_robots1.jpg" style="width: 100%px;" class="fr-fic fr-dib">Itai Cohen, center, professor of Physics and Design Tech, works with Melody Lim, left, and Zexi Liang, right, at Cohen’s lab in the Physical Sciences Building. Jason Koski/Cornell UniversityControlled by magnets making a pinching motion, the robots can inch-worm forward on a solid surface. They can also “swim” through fluids using the same motion.The combination of maneuverability, flexibility and sub-diffractive optical technology create a significant advance in the field of robotics, the researchers said.“I’m really excited by this convergence of microrobotics and microoptics,” said co-author&nbsp;<a href="https://www.ece.cornell.edu/faculty-directory/francesco-monticone">Francesco Monticone</a>, associate professor of electrical and computer engineering in Cornell Engineering, who designed the optical diffractive elements and helped the team identify applications. “The miniaturization of robotics has finally reached a point where these actuating mechanical systems can interact with and actively shape light at the scale of just a few wavelengths – a million times smaller than a meter.”To magnetically drive robots at this scale, the team patterned the bots with hundreds of nanometer-scale magnets that have an equal volume of material but two different shapes – long and thin, or short and stubby. The idea, Cohen said, originated with Fudan University physicist Jizhai Cui.“The long, thin ones need a larger magnetic field to flip them from pointing one way to pointing the other, while the short, stubby ones need a smaller field,” Cohen said. “That means you can apply a big magnetic field to get them all aligned, but if you apply a smaller magnetic field, you only flip the short, stubby ones.”Cornell scientists combined this principle with very thin films invented at the Cornell Nanoscale Science and Technology Facility to create the robots.One of the main optical engineering challenges was figuring out the most suitable approach for three tasks – tuning light, focusing, and super-resolution imaging – for this specific platform, because “different approaches have different performance trade-offs depending on how the microrobot can move and change shape,” Monticone said.There’s a benefit to being able to mechanically move the diffracting elements in order to enhance imaging, Cohen said. The robot itself can be used as a diffraction grading, or a diffractive lens can be added. In this way, the robots can act as a local extension of the microscope lens looking down from above.The robots&nbsp;measure forces by using the same magnet-driven pinching motion that enables them to walk to push against structures.“These robots are very compliant springs. So as something pushes against them, the robot can squeeze,” Cohen said. “That changes the diffraction pattern, and we can measure that quite nicely.”Force-measurement and optical abilities can be applied in basic research, as in explorations of the structure of DNA, the researchers said. Or they might be deployed in a clinical setting.“Looking to the future, I can imagine swarms of diffractive microbots performing super-resolution microscopy and other sensing tasks while walking across the surface of a sample,” Monticone said. “I think we are really just scratching the surface of what is possible with this new paradigm marrying robotic and optical engineering at the microscale.”Contributing authors to the study are Zexi Liang, postdoctoral associate at LAASP; Melody X. Lim, experimental fellow at the Kavli Institute at Cornell for Nanoscale Science (KIC); and&nbsp;Mohamed I. Abdelrahman, doctoral student in&nbsp;electrical and computer engineering at Cornell Engineering.The research was made possible by the Cornell Center for Materials Research, the National Science Foundation and the Cornell NanoScale Science and Technology Facility.

Itai Cohen, professor of physics, in his lab in the Physical Sciences Building. Jason Koski/Cornell University

Cornell researchers in physics and engineering have created the smallest walking robot yet.

Smallest walking robot makes microscale measurements

In 2018, Google DeepMind's AlphaZero program taught itself the games of chess, shogi, and Go using machine learning and a special algorithm to determine the best moves to win a game within a defined grid. Now, a team of Caltech researchers has developed an analogous algorithm for autonomous robots—a planning and decision-making control system that helps freely moving robots determine the best movements to make as they navigate the real world."Our algorithm actually strategizes and then explores all the possible and important motions and chooses the best one through dynamic simulation, like playing many simulated games involving moving robots," says&nbsp;<a href="https://www.eas.caltech.edu/people/sjchung">Soon-Jo Chung</a>, Caltech's Bren Professor of Control and Dynamical Systems and a senior research scientist at JPL, which Caltech manages for NASA. "The breakthrough innovation here is that we have derived a very efficient way of finding that optimal safe motion that typical optimization-based methods would never find."The team describes the technique, which they call Spectral Expansion Tree Search (SETS), in the December&nbsp;<a href="https://www.science.org/doi/10.1126/scirobotics.ado1010">cover article</a> of the journal&nbsp;Science Robotics.Many robots can move quite freely and in any direction. Consider, for example, a humanoid robot designed to assist an elderly person in a home. Such a robot should be able to move in many different ways and, essentially, in any direction within the space as it encounters obstacles or unexpected events while completing its tasks. That robot's set of movements, obstacles, and challenges will be very different from those of a self-driving car, for example.How, then, can a single algorithm guide different robotic systems to make the best decisions to move through their surroundings?"You don't want a designer to have to go in and handcraft these motions and say, 'This is the discrete set of moves the robot should be able to do,'" says John Lathrop, a graduate student in control and dynamical systems at Caltech and co-lead author of the new paper. "To overcome this, we came up with SETS."SETS uses control theory and linear algebra to find natural motions that use a robotic platform's capabilities to its fullest extent in a physical setting.The basic underlying concept is based on a Monte Carlo Tree Search, a decision-making algorithm also used by Google's AlphaZero. Here, Monte Carlo essentially means something random, and tree search refers to navigating a branching structure that represents the relationships of data in a system. In such a tree, a root branches off to so-called child nodes that are connected by edges. Using Monte Carlo Tree Search for a game like Go, possible moves are represented as new nodes, and the tree grows larger as more random samples of possible trajectories are attempted. The algorithm plays out the possible moves to see the final outcomes of the different nodes and then selects the one that offers the best outcome based on a point valuation.The problem, Lathrop explains, is that when using this branching tree structure for continuous dynamical systems such as robots operating in the physical world, the total number of trajectories in the tree grows exponentially. "For some problems, trying to simulate every single possibility and then figure out which one is best would take years, maybe hundreds of years," he says.To overcome this, SETS takes advantage of an exploration/exploitation trade-off. "We want to try simulating trajectories that we haven't investigated before—that's exploration," Lathrop says. "And we want to continue looking down paths that have previously yielded high reward—that's exploitation. By balancing the exploration and the exploitation, the algorithm is able to quickly converge on the optimal solution among all possible trajectories."For example, if a robot starts out calculating a couple of possible actions that it determines would cause it to smash into a wall, there is no need for it to investigate any of the other nodes on that branch of the tree."This exploration/exploitation tradeoff and search over the robot's natural motions enables our robots to think, move, and adapt to new information in real-time," says Benjamin Rivière (PhD '24), a postdoctoral scholar research associate in mechanical and civil engineering at Caltech and co-lead author of the paper.SETS can run an entire tree search in about a tenth of a second. During that time, it can simulate thousands to tens of thousands of possible trajectories, select the best one, and then act. The loop continues over and over, giving the robotic system the ability to make many decisions each second.A key feature of the SETS algorithm is that it can be applied to essentially any robotic platform. The features and capabilities do not have to be programmed individually. In the new paper, Chung and his colleagues demonstrate the algorithm's successful utility in three completely different experimental settings—something that is very rare in robotics papers.In the first, a quadrotor drone was able to observe four hovering white balls while avoiding four orange balls, all while navigating an airfield rife with randomly occurring, dangerous air currents, or thermals. The drone experiment was conducted at Caltech's&nbsp;<a href="https://cast.caltech.edu/">Center for Autonomous Systems and Technologies</a> (CAST). In the second, the algorithm augmented a human driver of a tracked ground vehicle to navigate a narrow and winding track without hitting the siderails. And in the final setup, SETS helped a pair of tethered spacecraft capture and redirect a third agent, which could represent another spacecraft, an asteroid or another object.A team of Caltech students and researchers are currently applying a version of the SETS algorithm to an Indy car that will participate in the Indy Autonomous Challenge at the Consumer Electronics Show (CES) in Las Vegas on January 9.The work was supported by the Defense Advanced Research Projects Agency's Learning Introspective Control (LINC) program, the Aerospace Corporation, and Supernal, and is in part based on work supported by the National Science Foundation Graduate Research Fellowship Program.

A team of Caltech researchers has developed an analogous algorithm for autonomous robots—a planning and decision-making control system that helps freely moving robots determine the best movements to make as they navigate the real world.

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