An improved local path planning algorithm based on kinematics analysis of the Skid-Steered wheeled vehicle

Path planning research takes a significant position in the field of driverless driving, especially local path planning is a key point to ensure the safety of driverless vehicles. At present, the local path planning algorithm is mainly designed for the ackermann vehicle. However, due to the different steering principles, the local path planning algorithm based on the Ackerman steering principle is not suitable for the skid-steered wheeled vehicle. The local path planning algorithm does not consider the kinematics of the skid-steered wheeled vehicle. In this paper, the skid-steered wheeled vehicle is simplified to a single-axle model and the steering kinematics characteristic is analyzed. The classical Dynamic Window Approach (DWA) is improved based on the analysis of the kinematics characteristic of the skid-steered wheeled vehicle with the shortest passing time as the goal. The improved algorithm combines the available velocities of the right and left wheels at the next moment, and finally chooses the combination that takes the shortest time to avoid the obstacle. The planned local path can meet the steering requirements of the skid-steered wheeled vehicle. The improved algorithm is simulated in MATLAB, and the time passed in the map of 10m range is 42.1s.