Simulation of Robotic Peg-in-hole Assembly Strategy Based on DRL

doi:10.16182/j.issn1004731x.joss.23-0518

Abstract

Abstract:

Aiming at the existing peg-in-hole assembly method problems of dependence on accurate contact state models, difficulties in data acquisition, low sampling efficiency, and poor security, a simulation research method for robot peg-in-hole assembly strategy based on DRL is proposed. A simulation environment of robot peg-in-hole assembly based on ROS-Gazebo is built, and a method of gravity compensation for force/torque sensor based on a least square method is proposed. The reinforcement learning paradigm is employed to model the robot peg-in-hole assembly, and a method based on soft actor-critic(SAC) algorithm is proposed. The communication mechanism between the simulation environment and the deep reinforcement learning algorithm is established through ROS. Simulation experiments show that the proposed SAC algorithm enables robots to accomplish the peg-in-hole assembly task autonomously and compliantly with good generalization ability.

Key words: peg-in-hole assembly, DRL, compliance control, assembly strategy simulation, ROS-Gazebo simulation environment

CLC Number:

TP391.9

Zhu Zilu, Liu Yongkui, Zhang Lin, Wang Lihui, Lin Tingyu. Simulation of Robotic Peg-in-hole Assembly Strategy Based on DRL[J]. Journal of System Simulation, 2024, 36(6): 1414-1424.

Figures/Tables 12

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参数名称	参数值
经验回放池尺寸	10⁶
批量尺寸	256
奖励折扣因子	0.99
软更新因子	0.01
学习率	3×10^-4
目标熵	-2

实验设置	装配时间/s	装配成功率/%
A0+B0+C0	6.27	100
A0+B0+C1	6.09	99
A0+B1+C0	6.33	100
A0+B1+C1	7.94	98
A1+B0+C0	5.88	100
A1+B0+C1	5.41	100
A1+B1+C0	6.01	100
A1+B1+C1	7.61	99

算法	训练时间/h	实验设置	装配成功率/%	装配时间/s	最大接触力/N	最大接触力矩/(N·m)
SAC	6.5	B0	99	6.09	5	0.5
SAC	6.5	B1	98	7.94	5	0.7
DDPG	5.8	B0	100	3.25	50	7.0
DDPG	5.8	B1	74	3.56	65	8.0
PPO	4.3	B0	93	11.79	5	0.8
PPO	4.3	B1	75	14.02	30	3.5

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