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Multiple peg-in-hole compliant assembly based on a learning-accelerated deep deterministic policy gradient strategy
Industrial Robot ( IF 1.8 ) Pub Date : 2021-07-27 , DOI: 10.1108/ir-01-2021-0003
Xinwang Li 1 , Juliang Xiao 1 , Wei Zhao 1 , Haitao Liu 1 , Guodong Wang 1
Affiliation  

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.



中文翻译:

基于学习加速的深度确定性策略梯度策略的多钉孔兼容装配

目的

由于传统装配策略需要对接触模型进行复杂的分析,因此机器人自主完成多个钉孔装配任务仍然是一个挑战。本文旨在使机器人能够自主更高效地完成组装任务,结合强化学习(RL)学习的策略,提出了一种学习加速的深度确定性策略梯度(LADDPG)算法。

设计/方法/方法

多钉孔装配策略设计为两个模块:高级规划模块和底部控制模块。高级模块由 LADDPG 代理完成,用于根据几何和环境约束导出高级命令,即所需的接触力。底层控制模块会根据高级模块下达的指令集,通过自适应阻抗算法驱动机器人完成顺应装配任务。此外,还开发了一套安全保障机制,以安全地训练协作机器人完成自主学习。

发现

该方法可以通过强化学习很好地完成装配任务,并且可以实现机器人对环境的良好依从性。与原始DDPG算法相比,装配过程中瞬时最大接触力和接触扭矩的平均值分别降低了约38%和74%。

实际影响

整个算法也可以应用于其他机器人,装配策略可以应用于自动装配领域。

原创性/价值

提出了一种基于 LADDPG 算法的兼容装配策略来完成自动化的多钉孔装配任务。

更新日期:2021-07-27
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