Work

A Motion Control Strategy for a Blind Hexapod Robot Based on Reinforcement Learning and Central Pattern Generator

Year: 2025

Type: article

Abstract: Hexapod robots that use external sensors to sense the environment are susceptible to factors such as light intensity or foggy weather. This effect leads to a drastic decrease in the motility of the hexapod robot. This paper proposes a motion control strategy for a blind hexapod robot. The hexapod robot is symmetrical and its environmental sensing capability is obtained by collecting proprioceptive signals from internal sensors, allowing it to pass through rugged terrain without the need for external sensors. The motion gait of the hexapod robot is generated by a central pattern generator (CPG) network constructed by Hopf oscillators. This gait is a periodic gait controlled by specific parameters given in advance. A policy network is trained in the target terrain using deep reinforcement learning (DRL). The trained policy network is able to fine-tune specific parameters by acquiring information about the current terrain. Thus, an adaptive gait is obtained. The experimental results show that the adaptive gait enables the hexapod robot to stably traverse various complex terrains.

Source: Symmetry

DOI: 10.3390/sym17071058

Authors: Lei Wang, Ruiwen Li, Xiaoxiao Wang, Weidong Gao, Yiyang Chen

Institutions: Wuxi University, Nanjing University of Information Science and Technology, Soochow University

Language: English

Topic: Robotic Locomotion and Control

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Basic Research Program of Jiangsu Province