This work presents the experimental assessment of a hybrid control scheme based on Deep Reinforcement. Learning (DRL) for obstacle avoidance in robot manipulators. More precisely, relying on an equivalent Linear Parameter Varying (LPV) state-space representation of the system, two operative modes, one based on both joint positions and velocities, one only based on velocity inputs, are activated depending on the measurement of the distance between the robot and the obstacle. Therefore, when the obstacle is close to the robot, a switching mechanism is introduced to enable the DRL algorithm instead of the basic motion planner, thus giving rise to a self-configuring architecture to cope with objects randomly moving in the workspace. The experimental tests of the DRL based collision avoidance hybrid strategy are carried out 011 a physical EPSON VT6 robot manipulator with satisfactory results.
Experimental assessment of deep reinforcement learning for robot obstacle avoidance: a LPV control perspective
2021-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
Towards Dynamic Obstacle Avoidance for Robot Manipulators with Deep Reinforcement Learning
Springer Verlag | 2022
|Shared control of robot manipulators with obstacle avoidance: a deep reinforcement learning approach
BASE | 2023
|Towards monocular vision based obstacle avoidance through deep reinforcement learning
BASE | 2020
|