Optimal Robot Motion Planning in Constrained Workspaces Using Reinforcement Learning

被引：12
|
作者：
Rousseas, Panagiotis [1 ]
Bechlioulis, Charalampos P. [1 ]
Kyriakopoulos, Kostas J. [1 ]
机构：
[1] Natl Tech Univ Athens, Sch Mech Engn, Control Syst Lab, Athens, Greece
来源：
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2020年
关键词：
TIME OBSTACLE AVOIDANCE; PIANO MOVERS PROBLEM; NAVIGATION;
D O I：
10.1109/IROS45743.2020.9341148
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to adjust appropriately the parameters of the underlying potential field towards minimizing the Hamilton-Jacobi-Bellman (HJB) error. The proposed method, outperforms consistently a Rapidly-exploring Random Trees (RRT*) method and consists a fertile advancement in the optimal motion planning problem.
引用
收藏
页码：6917 / 6922
页数：6
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