Optimal Robot Motion Planning in Constrained Workspaces Using Reinforcement Learning

被引：15

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

页码：6917 / 6922

页数：6

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