Slope-Steering Motion Planning for Unmanned Tracked Vehicles Based on SSTP-RRT

Abstract

Motion planning algorithms for unmanned tracked vehicles (UTV) which travel on off-road terrain often suffer from low accuracy and poor robustness when confronted with track sliding factor. SSTP-RRT (Slope-Steering Trajectory Parameter-space Rapidly-exploring Random Tree) motion planning algorithm is proposed for the slope-steering motion planning of UTV while considering the track sliding factor. A modified tracked vehicle slope-steering mechanical model is established to describe the process of UTV steering on the slope. The proposed UTV slope-steering model considers combined horizontal and vertical track sliding as well as steering centrifugal force. The PSO-LM (Particle Swarm Optimization - Levenberg Marquardt) algorithm is proposed to solve how to choose the initial values for the solution of the nonlinear system of equations of the model. The vehicle velocity, steering radii, and heading angle are taken as the independent variables, and the output rotational velocities of the inner and outer sprockets are taken as the dependent variables. The data are generated in a pre-computed way, and the output can be acquired by the point cloud surface fitting method based on moving least squares. By this method, UTV can travel according to the planned trajectory on the slope precisely and duly.

Keywords

• Unmanned tracked vehicle
• slope steering
• motion planning
• SSTP-RRT