Intelligent Path Planning for Mobile Robots Based on SAC Algorithm

Authors

Laiyi Yang, School of Software Engineering in Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFollow
Jing Bi, School of Software Engineering in Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFollow
Haitao Yuan, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Abstract

Abstract: Aiming at the high dimension, slow convergence and complex modelling of traditional path planning algorithms for mobile robots, a new intelligent path planning algorithm is proposed, which is based on deep reinforcement learning soft actor-critic (SAC) algorithm to save the poor performance of robot in complicated environments with static and dynamic obstacles. An improved reward function is designed to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority experience pool techniques. To evaluate the performance, a pygame-based simulation environment is constructed. Compared with proximal policy optimization(PPO) algorithm, experimental results show that the cumulative reward of the proposed method is much higher than that of PPO, and the more robust than PPO.

Recommended Citation

Yang, Laiyi; Bi, Jing; and Yuan, Haitao (2023) "Intelligent Path Planning for Mobile Robots Based on SAC Algorithm," Journal of System Simulation: Vol. 35: Iss. 8, Article 9.
DOI: 10.16182/j.issn1004731x.joss.22-0412
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss8/9

First Page

1726

Last Page

1736

CLC

TP311.5

Recommended Citation

Yang Laiyi, Bi Jing, Yuan Haitao. Intelligent Path Planning for Mobile Robots Based on SAC Algorithm[J]. Journal of System Simulation, 2023, 35(8): 1726-1736.

Corresponding Author

Bi Jing

DOI

10.16182/j.issn1004731x.joss.22-0412