首页|Study Data from Zhejiang University Update Understanding of Robotics and Automat ion (Somtp: a Self-supervised Learningbased Optimizer for Mpc-based Safe Trajec tory Planning Problems In Robotics)
Study Data from Zhejiang University Update Understanding of Robotics and Automat ion (Somtp: a Self-supervised Learningbased Optimizer for Mpc-based Safe Trajec tory Planning Problems In Robotics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news originating from Hangzhou, Peop le’s Republic of China, by NewsRx correspondents, research stated, “Model Predic tive Control (MPC)-based trajectory planning has been widely used in robotics, a nd incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizer s are resource-consuming and slow to solve such non-convex constrained optimizat ion problems (COPs) while learning-based methods struggle to satisfy the non-con vex constraints.”
HangzhouPeople’s Republic of ChinaAs iaRobotics and AutomationRoboticsAlgorithmsEmerging TechnologiesMachin e LearningSupervised LearningZhejiang University
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