兵工自动化2024，Vol.43Issue(6)：91-96,6.DOI:10.7690/bgzdh.2024.06.022
基于深度强化学习的机械臂动态目标抓取方法
A Dynamic Target Grasping Method for Manipulator Based on Deep Reinforcement Learning
张轩 1卢惠民 2任君凯 2莫新民 1肖浩然 2张伟杰 1杨璇1
作者信息
- 1. 西北机电工程研究所人体增强技术创新中心,陕西 咸阳 712099
- 2. 国防科技大学智能科学学院,长沙 410073
- 折叠
摘要
Abstract
Aiming at the problems of trajectory planning difficulty,insufficient real-time performance and difficulty in realizing six-degree-of-freedom grasping of existing manipulator dynamic target grasping methods,a manipulator dynamic target grasping method based on deep reinforcement learning(DRL)is proposed.The Markov decision process(MDP)is modeled,and the state space,action space and reward function are designed to realize the six-degree-of-freedom grasping of the dynamic target by the manipulator.Based on Pybullet,the dynamic target grasping simulation test environment of manipulator is constructed,and the method is trained.The trained strategy is tested in a novel scene,and compared with the dynamic target grasping method of classical planning control.The simulation results show that the method can realize the six-degree-of-freedom grasping of the dynamic target by the manipulator,and has advantages in grasping success rate and speed.关键词
动态目标抓取/马尔科夫/轨迹规划/深度强化学习/六自由度抓取Key words
dynamic target grasping/Markov/trajectory planning/deep reinforcement learning/six-degree-of-freedom grasping分类
信息技术与安全科学引用本文复制引用
张轩,卢惠民,任君凯,莫新民,肖浩然,张伟杰,杨璇..基于深度强化学习的机械臂动态目标抓取方法[J].兵工自动化,2024,43(6):91-96,6.
