运动规划
计算机科学
弹道
规划师
人工智能
托普西斯
数学优化
理想溶液
机器人
工程类
运筹学
数学
天文
热力学
物理
作者
Guangliang Liao,Chunyun Fu,Yinghong Yu,Kexue Lai,Beihao Xia,Jingkang Xia
标识
DOI:10.1109/tits.2024.3518915
摘要
Local trajectory planning (TP) for collision avoidance typically comprises path planning (PP) and velocity planning (VP). Various objectives must be fulfilled in a PP task, and the majority of prior works integrate all objectives into a unified cost function. To prioritize the dominant objectives of each PP stage, we propose to decouple the PP task into two separate subtasks, enabling the logical establishment of subtask-oriented segmented PP methods. First, based on risk evaluation of four vehicle vertices, an improved artificial potential field was established. Second, a novel transit point selection method was applied to decouple the PP task into two segmented subtasks. Then, the optimization problem was converted into a multi-attribute decision-making (MADM) problem and the technique for order preference by similarity to ideal solution (TOPSIS) method was utilized to obtain two optimal segmented paths. Finally, a velocity planner based on cubic polynomial, in conjunction with a support vector machine-based stability classifier, was designed. The proposed trajectory planner was then verified in six typical driving scenarios, including both simulation and real vehicle studies. Verification results demonstrate that the proposed planner effectively decouples the PP task and achieves a safe, comfortable, efficient, and trackable trajectory.
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