Path Planning Method for Manipulators Based on Improved Twin Delayed Deep Deterministic Policy Gradient and RRT*

Author:

Cai Ronggui1,Li Xiao12

Affiliation:

1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China

2. Key Laboratory of Intelligence Integrated Automation in Guangxi Universities, Guilin 541004, China

Abstract

This paper proposes a path planning framework that combines the experience replay mechanism from deep reinforcement learning (DRL) and rapidly exploring random tree star (RRT*), employing the DRL-RRT* as the path planning method for the manipulator. The iteration of the RRT* is conducted independently in path planning, resulting in a tortuous path and making it challenging to find an optimal path. The setting of reward functions in policy learning based on DRL is very complex and has poor universality, making it difficult to complete the task in complex path planning. Aiming at the insufficient exploration of the current deterministic policy gradient DRL algorithm twin delayed deep deterministic policy gradient (TD3), a stochastic policy was combined with TD3, and the performance was verified on the simulation platform. Furthermore, the improved TD3 was integrated with RRT* for performance analysis in two-dimensional (2D) and three-dimensional (3D) path planning environments. Finally, a six-degree-of-freedom manipulator was used to conduct simulation and experimental research on the manipulator.

Funder

Innovation Project of Guilin University of Electronic Technology (GUET) Graduate Education

Key Laboratory of Automatic Testing Technology and Instruments Foundation of Guangxi

Publisher

MDPI AG

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