Author:
Li Xinwang,Xiao Juliang,Zhao Wei,Liu Haitao,Wang Guodong
Abstract
Purpose
As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more efficiently, with the strategies learned by reinforcement learning (RL), a learning-accelerated deep deterministic policy gradient (LADDPG) algorithm is proposed.
Design/methodology/approach
The multiple peg-in-hole assembly strategy is designed in two modules: an advanced planning module and a bottom control module. The advanced module is completed by the LADDPG agent, which is used to derive advanced commands based on geometric and environmental constraints, that is, the desired contact force. The bottom-level control module will drive the robot to complete the compliant assembly task through the adaptive impedance algorithm according to the command set issued by the advanced module. In addition, a set of safety assurance mechanisms is developed to safely train a collaborative robot to complete autonomous learning.
Findings
The method can complete the assembly tasks well through RL, and it can realize satisfactory compliance of the robot to the environment. Compared with the original DDPG algorithm, the average values of the instantaneous maximum contact force and contact torque during the assembly process are reduced by approximately 38% and 74%, respectively.
Practical implications
The entire algorithm can also be applied to other robots and the assembly strategy can be applied in the field of the automatic assembly.
Originality/value
A compliant assembly strategy based on the LADDPG algorithm is proposed to complete the automated multiple peg-in-hole assembly tasks.
Subject
Industrial and Manufacturing Engineering,Computer Science Applications,Control and Systems Engineering
Reference29 articles.
1. Variable compliance control for robotic peg-in-hole assembly: a deep-reinforcement-learning approach;Applied Sciences,2020
2. Learning force control for contact-rich manipulation tasks with rigid position-controlled robots;IEEE Robotics and Automation Letters,2020
3. Brittain, M., Bertram, J., Yang, X. and Wei, P. (2019), “Prioritized sequence experience replay”, available at: https://arxiv.org/abs/1905.12726
4. Learning variable impedance control;The International Journal of Robotics Research,2011
5. A learning framework for high precision industrial assembly,2019
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献