强化学习
计算机科学
控制器(灌溉)
控制工程
断层(地质)
执行机构
容错
人工智能
光学(聚焦)
控制系统
控制(管理)
可编程逻辑控制器
分布式计算
工程类
物理
地质学
光学
地震学
电气工程
操作系统
生物
农学
作者
Jonas Zinn,Birgit Vogel‐Heuser,Mirko Gruber
出处
期刊:Journal of Mechanical Design
日期:2021-04-08
卷期号:143 (7)
被引量:6
摘要
Abstract Fault-tolerant control policies that automatically restart programable logic controller-based automated production system during fault recovery can increase system availability. This article provides a proof of concept that such policies can be synthesized with deep reinforcement learning. The authors specifically focus on systems with multiple end-effectors that are actuated in only one or two axes, commonly used for assembly and logistics tasks. Due to the large number of actuators in multi-end-effector systems and the limited possibilities to track workpieces in a single coordinate system, these systems are especially challenging to learn. This article demonstrates that a hierarchical multi-agent deep reinforcement learning approach together with a separate coordinate prediction module per agent can overcome these challenges. The evaluation of the suggested approach on the simulation of a small laboratory demonstrator shows that it is capable of restarting the system and completing open tasks as part of fault recovery.
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