A visual path-following learning approach for industrial robots using DRL
| Autor: | Ismael Lopez-Juarez, Reyes Rios-Cabrera, Alan de Jesus Maldonado-Ramirez |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Process (engineering) Orientation (computer vision) General Mathematics 020208 electrical & electronic engineering Control engineering 02 engineering and technology Business agility computer.software_genre Industrial and Manufacturing Engineering Computer Science Applications law.invention Domain (software engineering) Industrial robot 020901 industrial engineering & automation Control and Systems Engineering law Virtual machine 0202 electrical engineering electronic engineering information engineering Robot Reinforcement learning computer Software |
| Zdroj: | Robotics and Computer-Integrated Manufacturing. 71:102130 |
| ISSN: | 0736-5845 |
| DOI: | 10.1016/j.rcim.2021.102130 |
| Popis: | Manufacturing companies are in constant need for improved agility. An adequate combination of speed, responsiveness, and business agility to cope with fluctuating raw material costs is essential for today’s increasingly demanding markets. Agility in robots is key in operations requiring on-demand control of a robot’s tool position and orientation, reducing or eliminating extra programming efforts. Vision-based perception using full-state or partial-state observations and learning techniques are useful to create truly adaptive industrial robots. We propose using a Deep Reinforcement Learning (DRL) approach to solve path-following tasks using a simplified virtual environment with domain randomisation to provide the agent with enough exploration and observation variability during the training to generate useful policies to be transferred to an industrial robot. We validated our approach using a KUKA KR16HW robot equipped with a Fronius GMAW welding machine. The path was manually drawn on two workpieces so the robot was able to perceive, learn and follow it during welding experiments. It was also found that small processing times due to motion prediction (3.5 ms) did not slow down the process, which resulted in smooth robot operations. The novel approach can be implemented onto different industrial robots to carry out different tasks requiring material deposition. |
| Databáze: | OpenAIRE |
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