Deep Reinforcement Learning-Based Path Planning for Multi-Arm Manipulators with Periodically Moving Obstacles

Applied Sciences

Ji-Hun Bae ¹  Jae-Han Park ¹  Evan Prianto ²  andJung-Su Kim ² 

[1] Applied Robot R&D Department, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Korea;Research Center for Electrical and Information Technology, Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea;

关键词: path planning;    multi-arm manipulators;    moving obstacles;    reinforcement learning;    soft actor–critic (SAC);    hindsight experience replay (HER);   

DOI  :  10.3390/app11062587

来源: DOAJ

【 摘 要 】

In the workspace of robot manipulators in practice, it is common that there are both static and periodic moving obstacles. Existing results in the literature have been focusing mainly on the static obstacles. This paper is concerned with multi-arm manipulators with periodically moving obstacles. Due to the high-dimensional property and the moving obstacles, existing results suffer from finding the optimal path for given arbitrary starting and goal points. To solve the path planning problem, this paper presents a SAC-based (Soft actor–critic) path planning algorithm for multi-arm manipulators with periodically moving obstacles. In particular, the deep neural networks in the SAC are designed such that they utilize the position information of the moving obstacles over the past finite time horizon. In addition, the hindsight experience replay (HER) technique is employed to use the training data efficiently. In order to show the performance of the proposed SAC-based path planning, both simulation and experiment results using open manipulators are given.

【 授权许可】

Unknown