Robotics & Machine Learning Daily News2024,Issue(Nov.1) :41-42.

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)

Robotics & Machine Learning Daily News2024,Issue(Nov.1) :41-42.

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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Abstract

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.”

Key words

Hangzhou/People’s Republic of China/As ia/Robotics and Automation/Robotics/Algorithms/Emerging Technologies/Machin e Learning/Supervised Learning/Zhejiang University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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