<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

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

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This article delves into what is a cobot, exploring its architecture, programming interfaces, control systems, safety protocols, intricate mechanics, and integration capabilities of modern cobots.

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.

Helping Robots Make Good Decisions in Real Time

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The Cycloidal-Augmented Wheel (CLAW) is the latest innovation from the Adaptive Robotics and Technology Lab in the <a href="https://engineering.tamu.edu/etid/index.html" rel="noopener" target="_blank">Department of Engineering Technology and Industrial Distribution</a>. This novel mechanism allows a robot to maintain smooth wheeled motion on flat landscapes and seamlessly transition to a climbing motion to overcome obstacles. The wheel’s claw-like components extend to reach heights and retract when approaching the ground, allowing continuous contact with the surface.&nbsp;The CLAW was invented by <a href="https://engineering.tamu.edu/etid/profiles/lee-kiju.html" rel="noopener" target="_blank">Dr. Kiju Lee</a>, an associate professor in the Department of Engineering Technology and Industrial Distribution and J. Mike Walker ’66 Department of Mechanical Engineering, with mechanical engineering Ph.D. student Yuan Wei. As part of a 2020-2021 DARPA-funded project, Lee’s research team created the Adaptable Wheel-and-Leg Transformable Robot (<a href="https://engineering.tamu.edu/news/2022/05/etid-meen-lee-presents-transformable-robot-at-jeff-bezos-mars-conference.html" rel="noopener" target="_blank">a-WaLTR</a>). Unlike a-WaLTR, which switches between wheels and legs, the CLAW mechanism can be retrofitted on track robots or conventional wheeled robots.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732667371675-News-ETID-CLAWdiagram-28Oct2024.jpg" style="width: 100%px;" class="fr-fic fr-dib">Claws extend to reach heights and retract when approaching the ground, keeping the wheel in continual contact with the surface. | Image: <cite id="isPasted">Courtesy of Dr. Kiju Lee.</cite>Funded by the USDA National Institute of Food and Agriculture through the National Robotics Initiative 3.0 program, the project first focused on creating a scalable and adaptive swarm of robots for diverse agriculture environments. With the addition of the CLAW, these robots can manage an agricultural field’s varying topography and changing weather conditions. The design may also broaden the application of robots in search and rescue, disaster response, and military operations. Wei’s parents, who are physicians, even see its potential use in wheelchairs.&nbsp;To test the CLAW, Lee and Wei constructed CLAWbot, a fixed-axis, four-wheeled robot fitted with the new design. The robot demonstrated multi-terrain mobility and reliable climbing of obstacles and stairs. CLAWbot leverages existing research on wheeled robot control, localization, and navigation, which provides a solid foundation for developing self-operating abilities.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732667393017-News-ETID-CLAW-28Oct2024.jpg" style="width: 100%px;" class="fr-fic fr-dib">CLAWbot easily tackles a barrier in its path. | Image:&nbsp;<cite id="isPasted">Courtesy of Dr. Kiju Lee.</cite>The design was recently published in the IEEE/ASME Transactions on Mechatronics and the project has a provisional patent.“Design-driven innovation is the core inspiration of the Adaptive Robotics and Technology Lab. The CLAW mechanism exemplifies how novel mechanical design can solve the complex challenge of achieving versatile, multi-terrain mobility in mobile robots,” states Lee.

Inspired by airplane propellers, Yuan Wei proposed the initial idea which became the CLAW. | Image: Courtesy of Dr. Kiju Lee.

Texas A&M researchers transform how robots traverse challenging terrain.

Robots with CLAWS

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DEEP Robotics-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

DEEP Robotics-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

DEEP Robotics-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

robotics

robotics

Profiles