Heuristic Tentacle Algorithm for Local Path Planning Based on Obstacles Clustering Concept

Abstract

This paper introduces a heuristic tentacle algorithm for local path planning of unmanned skid-steering vehicle. Mobility, safety and economy are three mainly focused aspects in the navigation of unmanned ground vehicles. Critical skidding and slipping often occur during the turning motion, which effect the vehicle's motion apparently. So vehicle kinematics are discussed and applied to construct the cluster of tentacles. Several path assessment criteria named obstacle avoidance, terrain roughness and distance to the global path are discussed. Based on the multi-density clustering processed in the global path planning, heuristic method is introduced to guide to search in sparse region. The simulation analysis shows the generated local path can avoid the obstacles along the global path. Simultaneously. the global path can be smoothed through kinematic aware tentacle algorithm.