首页|Study Results from National University of Defense Technology Broaden Understanding of Robotics and Automation (Learningbased Near-optimal Motion Planning for Intelligent Vehicles With Uncertain Dynamics)
Study Results from National University of Defense Technology Broaden Understanding of Robotics and Automation (Learningbased Near-optimal Motion Planning for Intelligent Vehicles With Uncertain Dynamics)
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NETL
NSTL
IEEE
Current study results on Robotics - Robotics and Automation have been published. According to news reporting originating in Changsha, People’s Republic of China, by NewsRx journalists, research stated, “Motion planning has been an important research topic in achieving safe and flexible maneuvers for intelligent vehicles. However, it remains challenging to realize efficient and optimal planning in the presence of uncertain model dynamics.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the National University of Defense Technology, “In this paper, a sparse kernel-based reinforcement learning (RL) algorithm with Gaussian process (GP) regression (called GP-SKRL) is proposed to realize online adaptation and near-optimal motion planning performance. In this algorithm, we design an efficient sparse GP regression method to learn the uncertain dynamics. Based on the updated model, a sparse kernel-based policy iteration algorithm with an exponential barrier function is designed to learn the near-optimal planning policies with the capability to avoid dynamic obstacles. Thereby, batch-mode GP-SKRL with online adaption capability can estimate the changing system dynamics. The converged RL policies are then deployed on vehicles efficiently under a safety-aware module. As a result, the produced driving actions are safe and less conservative, and the planning performance has been noticeably improved. Extensive simulation results show that GP-SKRL outperforms several advanced motion planning methods in terms of average cumulative cost, trajectory length, and task completion time.”
ChangshaPeople’s Republic of ChinaAsiaRobotics and AutomationRoboticsAlgorithmsNational University of Defense Technology
NETL
NSTL
IEEE
