Browse Topic: Automation
Extreme winter weather often leads to ice accretion on transmission lines. Manual removal is inefficient, costly, and poses safety risks. To address this issue, this paper presents the design of a de-icing robot to replace manual operations for transmission line de-icing. The main content focuses on the detailed structural design of the robot, including the mobile platform, de-icing mechanism, and adaptive adjustment module. Finite element simulations are conducted on key components to verify the structural rationality and the correctness of material selection. The proposed de-icing robot enhances the safety of the de-icing process, improves operational efficiency, and provides a valuable reference for transmission line de-icing methods, demonstrating significant practical value.
With the development of controlled nuclear fusion technology, the tokamak device, as the most promising magnetic confinement fusion reactor for advanced engineering applications, requires remote maintenance of its internal components, which has become a key factor affecting both operational efficiency and safety. As a critical component directly exposed to high-temperature plasma, the divertor target plate needs to be periodically replaced and carefully maintained to ensure stable and reliable reactor operation. However, this region is subject to extreme conditions, including high temperature, high vacuum, and intense radiation, making conventional manual maintenance infeasible. This necessitates the development of intelligent and automated teleoperation systems. To address the automated assembly and disassembly requirements of divertor target plates, this study designs an integrated target plate actuator comprising key functional units: a positioning module, a screwing module, a quick-change module, and a passive compliance structure. The actuator achieves rapid and precise alignment with target plate holes, accommodates bolts of different specifications, and exhibits excellent impact resistance. Furthermore, stiffness and mechanical analyses, supported by finite element simulations, verify the actuator’s safety and reliability under high loads and impact forces. To further enhance operational performance, a segmented disassembly and assembly control strategy based on reinforcement learning is proposed, enabling the actuator to adaptively handle torque variations and ensure precise and stable bolt operations. The results demonstrate that the proposed actuator and control strategy significantly improve the accuracy, stability, and efficiency of target plate operations under complex working conditions, providing a reliable solution for automated divertor maintenance in tokamak devices.
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Humanoid robots have long been the focus of science fiction, but today they are making their way into industrial environments thanks to the simultaneous maturing and convergence of multiple systems. Technology advances have driven the development of humanoid robots that have a wide range of movement and can perform demanding jobs around the clock without tiring. While currently representing a small share of all industrial robot deployments, the humanoid robot market is projected to grow rapidly over the next few years. In fact, estimates suggest the market could reach over $4 billion by 2030. This growth is being driven by factors such as labor shortages, falling costs, and the need for more flexible automation.
Spinoff is NASA’s annual publication featuring successfully commercialized NASA technology. This commercialization has contributed to the development of products and services in the fields of health and medicine, consumer goods, transportation, public safety, computer technology, and environmental resources.
Physical AI refers to applications in which AI technologies are connected to hardware that sense and execute actions in the physical world, allowing systems to autonomously act and adapt in real time. It spans automotive, robotics, industrial automation, smart infrastructure, aerospace, healthcare devices, software-defined machines, and more. Regardless of application, they have one thing in common: they must operate safely, reliably, and predictably in real world environments. Unlike purely digital AI, these systems are constrained by embedded electronics, timing, power, safety, and system-level interactions that are difficult to validate early.
Robotic manipulation remains one of the harder unsolved problems in automation engineering. Vision-based systems have matured considerably — object localization, pose estimation, and grasp planning from RGB-D data are now reliable enough for structured industrial environments. What vision cannot provide is contact information: whether a grasp is stable, whether a surface is beginning to slip, or how force is distributed across a fingertip during a hold. These signals are what close the control loop during manipulation, and without them, systems compensate through excessive grip force, conservative motion profiles, and large training datasets designed to paper over sensing uncertainty.
Humanoid robots are moving beyond hype-driven prototypes toward early commercial deployment, with automotive manufacturing emerging as the first scalable adoption market, according to IDTechX’s recent report Humanoid Robots 2026-2036: Technologies, Markets, and Opportunities.
Automation has been a key part of manufacturing for over a century now, from the simple assembly lines of the past to the advanced, autonomous robotics of today. As the stresses placed on manufacturing systems continue to increase, however, the abilities of automated systems must increase as well. To meet the manufacturing demands of the 21st century, factory robotics must move beyond inflexible, hard-coded orders and gain the ability to quickly adapt to changing conditions — whether they be sudden business demands or new production requirements. This level of flexibility requires artificial intelligence (AI) certainly, but not just any AI; rather AI that can understand and interact with the real world. In other words, physical AI.
Traditional industrial robotics has been built on traditional premises: define the task precisely, program the motion, and repeat it with minimal variation. This model has delivered reliability, speed, and scale across multiple application domains.
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