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.
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 language | English |
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Title of host publication | 29th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2019 |
Number of pages | 8 |
Volume | 38 |
Publisher | Elsevier |
Publication date | 2019 |
Pages | 225-232 |
DOIs | |
Publication status | Published - 2019 |
Event | 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019) - Limerick, Ireland Duration: 24 Jun 2019 → 28 Jun 2019 https://faim2019.org |
Conference
Conference | 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019) |
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Country/Territory | Ireland |
City | Limerick |
Period | 24/06/2019 → 28/06/2019 |
Internet address |
Series | Procedia Manufacturing |
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ISSN | 2351-9789 |
Keywords
- Artificial Intelligence (AI)
- Reinforcement Learning
- ROS
- Robotics
- Deep Reinforcement Learning
- Simulation