A reinforcement learning enhanced pseudo-inverse approach to self-collision avoidance of redundant robots

Abstract

Introduction: Redundant robots offer greater flexibility compared to non-redundant ones but are susceptible to increased collision risks when the end-effector approaches the robot's own links. Redundant degrees of freedom (DoFs) present an opportunity for collision avoidance; however, selecting an appropriate inverse kinematics (IK) solution remains challenging due to the infinite possible solutions. Methods: This study proposes a reinforcement learning (RL) enhanced pseudo-inverse approach to address self-collision avoidance in redundant robots. The RL agent is integrated into the redundancy resolution process of a pseudo-inverse method to determine a suitable IK solution for avoiding self-collisions during task execution. Additionally, an improved replay buffer is implemented to enhance the performance of the RL algorithm. Results: Simulations and experiments validate the effectiveness of the proposed method in reducing the risk of self-collision in redundant robots. Conclusion: The RL enhanced pseudo-inverse approach presented in this study demonstrates promising results in mitigating self-collision risks in redundant robots, highlighting its potential for enhancing safety and performance in robotic systems.