Trajectory Planning of Quadruped Robot Over Obstacle with Single Leg Based on Deep Reinforcement Learning

Authors

Min Li, College of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Sen Zhang, Tangshan Research Institute, Southwest Jiaotong University, Tangshan 063000, China
Xiangguang Zeng, College of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Gang Wang, University of Electronic Science and Technology of China,Chengdu 610031
Tongwei Zhang, Tangshan Research Institute, Southwest Jiaotong University, Tangshan 063000, China
Dijie Xie, College of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Wenzhe Ren, College of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031
Tao Zhang, College of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Abstract

Abstract: Aiming at the problems of joint vibration and high energy consumption of quadruped robot in the process of walking over obstacles, a foot trajectory planning method of quadruped robot based on deep reinforcement learning SAC algorithm is proposed. Based on robot kinematics and Monte Carlo method, the motion space of the single-legged foot of quadruped robot is analyzed. A compound seventhdegree polynomial trajectory of the quadruped robot is planned. The SAC algorithm is used to train and obtain the low energy consumption obstacle crossing strategy of four-legged robot under different obstacle environment. The simulation results show that the compound seventh-degree polynomial trajectory planning can effectively reduce the joint vibration and foot contact force generated by the legs of the four-legged robot during obstacle crossing, and the robot can obtain the ideal trajectory planning parameters after the SAC algorithm training, and realize the stable walking over the obstacle with low energy consumption.

Recommended Citation

Li, Min; Zhang, Sen; Zeng, Xiangguang; Wang, Gang; Zhang, Tongwei; Xie, Dijie; Ren, Wenzhe; and Zhang, Tao (2025) "Trajectory Planning of Quadruped Robot Over Obstacle with Single Leg Based on Deep Reinforcement Learning," Journal of System Simulation: Vol. 37: Iss. 4, Article 6.
DOI: 10.16182/j.issn1004731x.joss.23-1535
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss4/6

First Page

895

Last Page

909

CLC

TP242；TP391.9

Recommended Citation

Li Min, Zhang Sen, Zeng Xiangguang, et al. Trajectory Planning of Quadruped Robot Over Obstacle with Single Leg Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2025, 37(4): 895-909.

DOI

10.16182/j.issn1004731x.joss.23-1535