Towards a Robot Simulation Framework for E-waste Disassembly Using Reinforcement Learning

Christoffer Brohus Kristensen, Frederik Arentz Sørensen, Hjalte Bjørn Dalgaard Brandt Nielsen, Martin Søndergaard Andersen, Søren Poll Bendtsen, Simon Bøgh

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

The purpose of this paper is to introduce a new framework for training and testing Reinforcement Learning (RL) algorithms for robotic unscrewing tasks. The paper investigates current disassembly technologies through a state-of-the-art analysis, and the basic concepts of reinforcement learning are studied. A comparable framework exists as an extension for OpenAI gym called Gym-Gazebo, which is tested and analysed. Based on this analysis, a design for a new framework is made to specifically support unscrewing operations in robotics disassembly of electronics waste.

The proposed simulation architecture uses ROS as data middleware, Gazebo (with the ODE physics solver) for simulating the robot environment, and MoveIt as a controller. The Gazebo simulation consists of a minimalistic setup in order to stay focused on the architecture and usability of the framework. The simulation world interfaces with the RL-agent, using OpenAI Gym and ROS-topics, which can be adapted to interface with a real robot. Lastly, the work demonstrates the functionality of the system by implementing an application example using a Q-learning algorithm, and the results of this are presented.
Original languageEnglish
Title of host publication29th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2019
Number of pages8
Volume38
PublisherElsevier
Publication date2019
Pages225-232
DOIs
Publication statusPublished - 2019
Event29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019) - Limerick, Ireland
Duration: 24 Jun 201928 Jun 2019
https://faim2019.org

Conference

Conference29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019)
CountryIreland
CityLimerick
Period24/06/201928/06/2019
Internet address
SeriesProcedia Manufacturing
ISSN2351-9789

Keywords

  • Artificial Intelligence (AI)
  • Reinforcement Learning
  • ROS
  • Robotics
  • Deep Reinforcement Learning
  • Simulation

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