A Guide-line and Key-point based A-star Path Planning Algorithm For Autonomous Land Vehicles
Erke Shang, Bin Dai, Yiming Nie, Qi Zhu, Xiao Liang, Dawei Zhao
Abstract
This paper presents a novel path planning algorithm for Autonomous Land Vehicles (ALVs), which makes two significant improvements to the traditional A-star algorithm. An evaluation standard is first introduced to measure the performance of different algorithms and to select appropriate parameters for the proposed algorithm. Then, a guide-line based A-star algorithm is presented, in which the guide-line is employed to develop the heuristic function to overcome the shortcoming of traditional A-star algorithms. Further, for improving the obstacle avoidance performance, a novel key-point based algorithm is presented, which would guide the planning path to avoid the obstacle much earlier than the traditional one. Combination of these two improvements, this improved A-Star based path planning algorithm is valid. Experimental results show that the performance of the proposed algorithm is robust and stable. Compared with the state-of-the-art techniques, the performance is better in both simulation and real application.