Locomotion Control and Gait Planning of a Novel Hexapod Robot Using Biomimetic Neurons

Abstract

This paper proposes a biomimetic method inspired by central pattern generator (CPG) for locomotion control and gait planning of a novel hexapod robot whose legs can radially free distributed around the robot body. The CPG network which consists of six CPG units based on the Matsuoka's neural oscillator is used to generate oscillatory signals. The phase signals outputted by CPG network are converted to tip trajectory signals of six legs by adjustment function, and the joint angles are computed by the inverse kinematics of the robot. The relevant parameters which decide the movement of robot are tuned according to the processed feedback information. Then, some experiments are performed to verify the effectiveness of the control method. The experimental results indicate that the proposed method succeeds in enabling a hexapod robot to walk steadily on rugged terrain. The developed hexapod robot has many potential applications for its agility, stable walking, and good environmental adaptability.