A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles
Zhuoren Li, Bo Leng, Chagen Luo, Liang Shao, Ruoling Yang, Chengen Tu, Arno Eichberger, Lu Xiong · 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) · 2022
In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the continuity of the derivative of the path curvature is used to generate an alternate path set. After collision detection, we use the analytic hierarchy process (AHP) to calculate the weights to choose the best path. Finally, to verify the efficiency of the proposed planner, an obstacle avoidance case is designed in the simulation. The simulation results show that the proposed planner can obviously avoid the obstacle well and has better smoothness than the tentacle approach that uses the clothoid curve. The total time of local planning within a single period is less than 20 ms, which still has great real-time performance.