首页|New Data from Harbin Institute of Technology Illuminate Research in Robotics (En hancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classif ication Approach)
New Data from Harbin Institute of Technology Illuminate Research in Robotics (En hancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classif ication Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on robotics is now availab le. According to news originating from Harbin, People's Republic of China, by Ne wsRx correspondents, research stated, "With the rise of electric vehicles, auton omous driving, and valet parking technologies, considerable research has been de dicated to automatic charging solutions. While the current focus lies on chargin g robot design and the visual positioning of charging ports, a notable gap exist s in addressing safety aspects during the charging plug-in process." Our news journalists obtained a quote from the research from Harbin Institute of Technology: "This study aims to bridge this gap by proposing a collision classi fication scheme for robot manipulators in automatic electric vehicle charging sc enarios. In situations with minimal visual positioning deviation, robots employ impedance control for effective insertion. Significant deviations may lead to po tential collisions with other vehicle parts, demanding discrimination through a global visual system. For moderate deviations, where a robot's end-effector enco unters difficulty in insertion, existing methods prove inadequate. To address th is, we propose a novel data-driven collision classification method, utilizing vi bration signals generated during collisions, integrating the robust light gradie nt boosting machine (LightGBM) algorithm. This approach effectively discerns the acceptability of collision contacts in scenarios involving moderate deviations. Considering the impact of passing vehicles introducing environmental noise, a n oise suppression module is introduced into the proposed collision classification method, leveraging empirical mode decomposition (EMD) to enhance its robustness in noisy charging scenarios."
Harbin Institute of TechnologyHarbinPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano- robotRobotRobotics
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