Adaptive path planning control of snake-like robot based on reinforcement tracking learning
Xiaojun Zhang · 2021
The path adaptive planning control of snake-like robot is influenced by uncertain disturbance of environment variables and joint mechanism components, which leads to poor stability of path planning parameter output. In order to improve the stability of path adaptive planning control of snake-like robot, a path adaptive planning control method of snake-like robot based on reinforcement tracking learning is proposed. The kinematics model of snake-like robot path adaptive planning is constructed, constraint parameter modeling of snake-like robot path adaptive planning control is carried out, path distribution parameters of snake-like robot are collected by using sensitive components such as attitude sensor information output instrument and accelerometer, and path spatial distribution parameters of snake-like robot are fused by using enhanced tracking learning method and input into time control actuator. Aiming at the error of unknown disturbance in path planning parameter control of snake-like robot, attitude parameter fusion and inertia error correction are carried out by using enhanced parameter fusion learning method, so as to realize stable control of snake-like robot path adaptive planning. Simulation results show that this method has good stability in path adaptive planning control of snake-like robot, strong positioning and tracking ability of attitude parameters, and improves the robustness of snake-like robot moving forward.