Landgraf Christian, Meese Bernd, Pabst Michael, Martius Georg, Huber Marco F
Fraunhofer Institute for Manufacturing, Engineering and Automation IPA, Nobelstraße 12, 70569 Stuttgart, Germany.
Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany.
Sensors (Basel). 2021 Mar 13;21(6):2030. doi: 10.3390/s21062030.
Manual inspection of workpieces in highly flexible production facilities with small lot sizes is costly and less reliable compared to automated inspection systems. Reinforcement Learning (RL) offers promising, intelligent solutions for robotic inspection and manufacturing tasks. This paper presents an RL-based approach to determine a high-quality set of sensor view poses for arbitrary workpieces based on their 3D computer-aided design (CAD). The framework extends available open-source libraries and provides an interface to the Robot Operating System (ROS) for deploying any supported robot and sensor. The integration into commonly used OpenAI Gym and Baselines leads to an expandable and comparable benchmark for RL algorithms. We give a comprehensive overview of related work in the field of view planning and RL. A comparison of different RL algorithms provides a proof of concept for the framework's functionality in experimental scenarios. The obtained results exhibit a coverage ratio of up to 0.8 illustrating its potential impact and expandability. The project will be made publicly available along with this article.
在小批量的高度灵活生产设施中，与自动化检测系统相比，人工检查工件成本高昂且可靠性较低。强化学习（RL）为机器人检测和制造任务提供了有前景的智能解决方案。本文提出了一种基于强化学习的方法，可根据任意工件的三维计算机辅助设计（CAD）确定一组高质量的传感器视角姿态。该框架扩展了可用的开源库，并提供了一个与机器人操作系统（ROS）的接口，用于部署任何受支持的机器人和传感器。集成到常用的OpenAI Gym和Baselines中，可为强化学习算法带来一个可扩展且具有可比性的基准。我们全面概述了视野规划和强化学习领域的相关工作。对不同强化学习算法的比较为该框架在实验场景中的功能提供了概念验证。所获得的结果显示覆盖率高达0.8，说明了其潜在影响和可扩展性。该项目将与本文一同公开。
