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A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks

 
: Landgraf, Christian; Meese, Bernd; Pabst, Michael; Martius, Georg; Huber, Marco

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Sensors. Online journal 21 (2021), No.6, Art. 2030, 17 pp.
http://www.mdpi.com/journal/sensors
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
English
Journal Article, Electronic Publication
Fraunhofer IPA ()
Bestärkendes Lernen; Simulation; Robotik; intelligente Sensorik; Inspektion

Abstract
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.

: http://publica.fraunhofer.de/documents/N-633751.html