UVeye
Israeli AI vehicle inspection startup founded 2016; drive-through systems scan undercarriage, tires, and exteriors in seconds. $380.5M raised.
UVeye is an Israeli AI-powered vehicle inspection company founded in 2016 by brothers Amir Hever and Ohad Hever and headquartered in Tel Aviv, Israel, with U.S. offices in Teaneck, New Jersey. The company was established after CEO Amir Hever, a computer vision expert, observed the inefficiency of manual undercarriage checks at Israeli security checkpoints and set out to automate the process. UVeye has raised approximately $380 million in total funding, including a $191 million round announced in January 2025 led by Woven Capital with participation from Toyota's fund and other institutional investors.
UVeye's product suite includes the Helios undercarriage scanning system, the Atlas 360-degree exterior inspection system, and the Artemis tire analysis system, all of which rely on proprietary hardware, sensor fusion, machine learning, and cloud-based algorithms. The systems can scan vehicles moving at speeds up to 25 mph and are designed to detect anomalies in bodywork, tires, exhaust systems, and chassis components without requiring vehicles to stop. The platform is deployed at dealerships, rental fleets, auction lots, seaports, and logistics hubs across North America and Europe, with installations at more than 400 locations.
Strategic partnerships include Amazon, which uses UVeye systems to inspect its delivery van fleet, and Hertz, which began deploying UVeye AI inspection at U.S. airport rental locations starting in Atlanta in 2025. Automotive OEM investors and partners include Toyota, General Motors, Hyundai, and Volvo. The company scans close to one million vehicles per month and was recognized by TIME and Fast Company among their best inventions lists.
Primary type & automation activities this supplier delivers:
Artemis
- Model: Artemis
- Launch Year: 2019
Automated tire inspection system that identifies tread depth, sidewall damage, and inflation issues during drive-through vehicle scanning.
Contact UVeye
WEBSITE
https://uveye.comPHONE
+972 3-911-3000HEADQUARTERS
Israel
Company Facts
Founded
2016
Primary Role
Manufacturer
Company Size
employees 100-500
Primary Region
Middle East
Annual Sales
-
Funding Stage
-
Funding Total
-
University of Southern Denmark Develops Touch-Based Robot Inspired by Star-Nosed Moles
The University of Southern Denmark's robotics team has proposed a concept robot that relies on touch rather than vision, inspired by the star-nosed mole. This mammal is known for its exceptional tactile sensitivity, using its unique nose structure to detect food with remarkable precision. The team's design mimics the functionality of the mole's Eimer organs, which are sensitive structures that allow the mole to perceive its environment through touch. This innovative approach is significant as it addresses the limitations of visual-based robotics, which struggle with transparent, obscured, or hidden objects. By utilizing a flexible liquid-filled tube equipped with pressure sensors, the robot can generate tactile signals in response to external pressure, offering a different sensory pathway compared to traditional visual systems. This could be particularly valuable for robots operating in dark or confined spaces where vision is inadequate. Currently, the prototype remains in the conceptual stage and has not been officially released. However, it signifies a shift towards multimodal perception in robotics, complementing visual information with tactile feedback. As robots are expected to navigate various unstructured environments, the integration of touch could enhance their operational capabilities, similar to the evolutionary adaptations seen in star-nosed moles.
Robotics Tactile Sensors Artificial Intelligence Sensor Technology
Faraday Future's Chris Chen Discusses Robotics as an Extension of Intelligent Vehicles
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Artificial Intelligence Automotive Features Robotics AIxCrypto artificial intelligence
Kazakhstan Police Utilizes Robotic Dogs for Patrol at Nomad Alemi Festival
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Robotic Dogs Public Safety Surveillance Technology Law Enforcement AI
Growing Demand for Robot Electronic Skin: Market Insights and Projections
The demand for electronic skin in humanoid robots is projected to reach 1.525 million square meters by 2030, with a market size of approximately 27.4 billion yuan. This shift indicates that electronic skin is transitioning from experimental samples to regular procurement items, as evidenced by a company receiving 13,000 orders for biomimetic skin in June alone. The significance of this demand lies in the evolving role of electronic skin, which enhances robots' capabilities in unstructured environments where visual data alone is insufficient. High-performance electronic skin, which collects various physical attributes, is essential for training models to understand object interactions, making it a critical component for robots operating in real-world scenarios. As humanoid robot manufacturers begin small-scale deliveries, the increase in electronic skin orders aligns with this trend. However, the integration of electronic skin with the design of the robots presents challenges, as factors like finger size and sensor layout must be collaboratively defined. The anticipated increase in demand and decrease in price hinges on the manufacturers' production capacity and the ability of electronic skin companies to maintain tactile precision while reducing costs.
Humanoid Robots Electronic Skin Tactile Sensors Robotics Technology
AGIBOT Introduces AGILE 2.0 for Enhanced Perception in Humanoid Robots
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robotics AI humanoid robots automation technology
MIT Researchers Develop New AI Technique for Safety-Critical Applications
MIT researchers have introduced a novel method that enhances generative artificial intelligence models for high-stakes problem-solving. This technique allows models to generate outputs that not only provide plausible solutions but also adhere to strict safety and task-specific requirements, known as hard constraints. By allowing more freedom during the generation process and enforcing constraints only on the final output, the method consistently delivers better solutions across various applications, including robotics and computer vision. The significance of this development lies in its potential to improve the reliability of AI in safety-critical environments. Traditional methods often struggle to balance the generative capabilities of AI with the necessity of meeting stringent safety standards. The new approach enables pretrained generative models to be adapted for use in scenarios where compliance with safety rules and physical laws is essential, thus expanding their applicability in real-world situations. Looking ahead, the adaptability of this technique suggests a promising future for generative AI in complex environments. As industries increasingly rely on AI for tasks like robot path planning, the ability to enforce hard constraints without compromising output quality will be crucial. No further timeline was disclosed at the time of publication.
Research Computer science and technology Artificial intelligence Machine learning Algorithms Mechanical engineering
SegCert-PCR: Certifiable Point Cloud Registration for Ground Segmentation in Unstructured Environments
The Journal of Field Robotics has published an early view article on SegCert-PCR, a method for certifiable point cloud registration focused on ground segmentation in unstructured field environments. This innovative approach addresses the challenges of accurately registering point clouds, which is crucial for various robotic applications in unpredictable terrains. The significance of SegCert-PCR lies in its potential to enhance the reliability and safety of robotic systems operating in complex environments. By ensuring certifiable registration of point clouds, this method can improve the performance of robots in tasks such as navigation, mapping, and environmental monitoring, where precision is paramount. Looking ahead, the adoption of SegCert-PCR could lead to advancements in robotic technologies that require robust ground segmentation capabilities. As the field of robotics continues to evolve, monitoring the implementation and effectiveness of this method will be essential for future developments in autonomous systems. No further timeline was disclosed at the time of publication.
RESEARCH ARTICLE
Tokyo University Innovates Artificial Skin for Enhanced Joint Angle Sensing in Robots
Tokyo University's research team has developed an artificial skin that enables robots to accurately sense joint angles through its deformation. This bionic skin, designed with a tissue-like layered structure, was tested on the Musashi-W humanoid robot. The results indicated that the average estimation error for joint angles could be maintained within approximately 3 degrees, and further accuracy was achievable by integrating skin and muscle information. This advancement is significant as traditional muscle-driven humanoid robots face challenges in measuring joint angles directly, unlike conventional robotic joints that utilize encoders. Current methods rely on limited sensors, such as muscle length and tension sensors, to infer joint angles. The research team's approach mimics biological structures by embedding numerous small sensors within soft tissues, enhancing the robot's ability to discern between external disturbances and internal state changes. Moving forward, the integration of these sensors into robotic systems could revolutionize how robots perceive their own movements and interactions with the environment. No further timeline was disclosed at the time of publication.
Humanoid Robots Proprioception Biomimetic Technology Robotic Sensors
Alien Control Systems Opens 191,000-Square-Foot Facility for Bullfrog Counter-Drone Production
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Military
UltraSense Proposes Ultrasound as a Solution for Tactile Intelligence in Robotics
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Artificial Intelligence / Cognition Grippers Human Robot Interaction / Haptics Humanoids Microprocessors / SoCs News
China’s Dexterous Hand Industry, 2026 Part 1 — The Demand Picture
MIR Databank puts China dexterous hand shipments above 30,000 units in 2026, yet only 3.5% of 2025 demand came from factory production. Part 1 of 3.
China’s Dexterous Hand Industry, 2026 Part 2 — The Technology
Linkage, tendon, direct drive or hybrid — and why hardware is converging while the DL1–DL5 manipulation scale decides who wins. Part 2 of 3.
ROBOTTODAY WEEKLY BRIEFING August 24 – 28, 2026
This week in robotics: XPeng's Dogotix raises over $900 million at a $6.3 billion valuation, Unitree's post-IPO slump revives bubble talk, Beijing's World Humanoid Robot Games close with a shift to real-world tasks, and Teradyne sues JAKA at Europe's Unified Patent Court. August 24–28, 2026.
WRC 2026 Core Components: 137 Robot Parts Suppliers
The third report in the WRC 2026 series. Report 1 mapped all 248 exhibitors by category and Report 2 covered the 139 that build finished robots. This one covers the 137 that build what goes inside them.
WRC 2026 Exhibitor Map: 248 Robotics Companies Mapped
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Handbook of Marine Craft Hydrodynamics and Motion Control Thor I. Fossen — RSF Specialist Shelf · Book S1 of 6
Added mass, wave loading, DVL navigation: the marine GNC reference for USV, ROV and AUV service engineers. The hardest book on the RSF list, 5/5.
Small Unmanned Aircraft: Theory and Practice Beard & McLain — RSF Specialist Shelf · Book S2 of 6
Beard & McLain derive the fixed-wing autopilot the RSF Top 10 never does: successive loop closure on roll, pitch, altitude and airspeed. 4/5.
Reinforcement Learning: An Introduction Richard S. Sutton & Andrew G. Barto — RSF Specialist Shelf · Book S5 of 6
“It's the AI” will not satisfy an OEM support call. Sutton & Barto turn that into something a service engineer can investigate. Free PDF, Part I 4/5.
Planning Algorithms Steven M. LaValle — RSF Specialist Shelf · Book S4 of 6
The reference that treats planning as a subject in its own right: configuration space, sampling-based methods, RRT. Free online, dual difficulty 4/5-2/5.
Underactuated Robotics Russ Tedrake — RSF Specialist Shelf · Book S3 of 6
Legged robots fall over for control reasons, not mechanical ones. Tedrake's free MIT 6.832 notes explain why, and what a controller must do about it.
