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 implemente

SUBMITTER: Hong T 

PROVIDER: S-EPMC11006967 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature