Path Planning and Gait Control of Snake Robot Based on PAPF and MPC

Abstract

The path planning and gait control of a snake-like robot is the key to achieving the autonomous task. Although the artificial potential field method can plan the optimal path, it is easy to fall into the local optimal solution, and it does not take into account the dynamic model of the snake-like robot, which is easy to cause the snake-like robot to have problems such as motion stagnation and sideslip. Aiming at the above problems, this paper proposes a path planning and gait autonomous generation algorithm for a snake-like robot based on predictive artificial potential field (PAPF) and model predictive control (MPC). This method uses the PAPF algorithm to model the surrounding environment of the snake-like robot, which is one of the constraints of MPC. In order to make the snake-like robot generate an effective gait pattern in line with the planned path, the MPC controller is designed based on the simplified model of the snake-like robot and considering the constraints of potential field and collision. The simulation results show that the method can independently generate effective gait patterns in line with the planned path under the constraints of obstacles and collisions.