Abstract
This article presents a novel path planning algorithm for autonomous land vehicles. There are four main contributions: Firstly, an evaluation standard is introduced to measure the performance of different algorithms and to select appropriate parameters for the proposed algorithm. Secondly, a guideline generated by human or global planning is employed to develop the heuristic function to overcome the shortcoming of traditional A-Star algorithms. Thirdly, for improving the obstacle avoidance performance, key points around the obstacle are employed, which would guide the planning path to avoid the obstacle much earlier than the traditional one. Fourth, a novel variable-step based A-Star algorithm is also introduced to reduce the computing time of the proposed algorithm. Compared with the state-of-the-art techniques, experimental results show that the performance of the proposed algorithm is robust and stable.
Highlights
Autonomous land vehicle (ALV), as a typical robot, is widely researched recently.[1]
The remaining of this article is organized as follows: the second section discusses some related works on path planning about ALVs, especially these graph search-based algorithms; the third section introduces the proposed evaluation standard for path planning algorithms; the fourth section describes the improved guideline-based A-Star algorithm; the fifth section describes the improved key point-based A-Star algorithm; the sixth section describes the improved variable-step based A-Star algorithm; the seventh section illustrates the results of real road environmental experiments and conclusions are drawn in the eighth section
The position of the vehicle is marked as a green circle, and the guideline front of the vehicle is marked as a red circle
Summary
Autonomous land vehicle (ALV), as a typical robot, is widely researched recently.[1]. The real results planned by the classical A-Star algorithm are shown as “A-Star without obstacle” and “A-Star with obstacle,” which are generated by the literature.[12] it is not A-Star’s fault, in many scenes, the road edge is not easy to be detected by perception. The remaining of this article is organized as follows: the second section discusses some related works on path planning about ALVs, especially these graph search-based algorithms; the third section introduces the proposed evaluation standard for path planning algorithms; the fourth section describes the improved guideline-based A-Star algorithm; the fifth section describes the improved key point-based A-Star algorithm; the sixth section describes the improved variable-step based A-Star algorithm; the seventh section illustrates the results of real road environmental experiments and conclusions are drawn in the eighth section
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