Path Planning for Multi-Arm Manipulators Using Soft Actor-Critic Algorithm with Position Prediction of Moving Obstacles via LSTM

被引：14

作者：

Park, Kwan-Woo ^{[1
]}

Kim, MyeongSeop ^{[1
,2
]}

Kim, Jung-Su ^{[1
]}

Park, Jae-Han ^{[2
]}

机构：

[1] Seoul Natl Univ Sci & Technol, Res Ctr Elect & Informat Technol, Dept Elect & Informat Engn, Seoul 01811, South Korea

[2] Korea Inst Ind Technol KITECH, Appl Robot R&D Dept, Ansan 15588, South Korea

来源：

APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 19期

基金：

新加坡国家研究基金会;

关键词：

path planning; multi-arm manipulators; moving obstacles; reinforcement learning; soft actor-critic (SAC); hindsight experience replay (HER); collision avoidance; long short-term memory (LSTM);

D O I：

10.3390/app12199837

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

This paper presents a deep reinforcement learning-based path planning algorithm for the multi-arm robot manipulator when there are both fixed and moving obstacles in the workspace. Considering the problem properties such as high dimensionality and continuous action, the proposed algorithm employs the SAC (soft actor-critic). Moreover, in order to predict explicitly the future position of the moving obstacle, LSTM (long short-term memory) is used. The SAC-based path planning algorithm is developed using the LSTM. In order to show the performance of the proposed algorithm, simulation results using GAZEBO and experimental results using real manipulators are presented. The simulation and experiment results show that the success ratio of path generation for arbitrary starting and goal points converges to 100%. It is also confirmed that the LSTM successfully predicts the future position of the obstacle.

引用

页数：20

共 52 条

[1] Accurate photovoltaic power forecasting models using deep LSTM-RNN [J].