Enhancing Hexapod Robot Locomotion Control Through PID Controller Optimization Using Genetic Algorithm

Abstract

Hexapod robots are highly stable due to their six legs and are commonly used in challenging terrain. Precise control of the joint angles of the legs is crucial for smooth locomotion and stability. In this study, we propose a Proportional-Integral-Derivative (PID) controller to regulate the positions of the joint angles. The PID controller adjusts the control output based on the error between the desired setpoint and the actual system state. To optimize the controller's performance, we employed a Genetic Algorithm, which improves the parameters of the controller through natural selection. We simulated the proposed method using SIMMECHANICS in MATLAB and demonstrated highly accurate tracking of the desired trajectory of the leg angles and significant stability of the robot during movement. The results indicate the proposed approach's effectiveness in regulating the hexapod robot's leg angles. The utilization of an optimized PID controller through the Genetic Algorithm in this study enhances the advancement of hexapod robots. By employing this method, we achieve automatic optimization, which significantly reduces the time and effort required to determine the best PID parameters. Consequently, this approach enhances the stability, control, and overall performance of the robot across diverse locomotion tasks.