首页|Studies from State Key Laboratory Provide New Data on Robotics (Manipulator Join t Fault Localization for Intelligent Flexible Manufacturing Based On Reinforceme nt Learning and Robot Dynamics)
Studies from State Key Laboratory Provide New Data on Robotics (Manipulator Join t Fault Localization for Intelligent Flexible Manufacturing Based On Reinforceme nt Learning and Robot Dynamics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "This article proposes a new method to addr ess the challenge of remote monitoring in intelligent flexible manufacturing sys tems. Specifically, we propose a multi working condition fault localization algo rithm for robotic arms, which eliminates the need for additional sensors and is based on the classic sliding window algorithm." Funders for this research include National Natural Science Foundation of China ( NSFC), Shanghai Municipal Science and Technology Major Project. Our news journalists obtained a quote from the research from State Key Laborator y, "We use reinforcement learning technology to learn detection parameter debugg ing experience under different working conditions, and combine the dynamics of t he robot to achieve fault detection and fault source localization in a flexible environment. Through the robot's own programmable logic controller system, the r emote monitoring system can sense the operating status of each link. To evaluate the effectiveness of our proposed method, we conducted experimental equipment s imulations and real-world industrial operations. The results show that under mul tiple operating conditions, the accuracy of fault detection reaches 86% , and the accuracy of localization reaches 81.35%. The deviation of results under different robot operating conditions is significantly lower than other algorithms. This study explores the potential and implementation approache s of reinforcement learning in intelligent manufacturing systems, with a particu lar focus on applications in flexible scenarios."
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