Adaptive Clustering Quasi-Line Search Path Planning Algorithm Based On Sampling

聚类分析 运动规划 数学优化 路径(计算) 采样(信号处理) 计算机科学 自适应采样 算法 最优化问题 数学 人工智能 蒙特卡罗方法 计算机视觉 滤波器(信号处理) 统计 程序设计语言 机器人
作者
Yifei Zhang,Zihao Zhang,Shiyuan Wang
出处
期刊:IEEE Transactions on Vehicular Technology [Institute of Electrical and Electronics Engineers]
卷期号:72 (2): 1720-1734 被引量:3
标识
DOI:10.1109/tvt.2022.3212982
摘要

In this paper, the adaptive clustering quasi-line search path planning algorithm (ACPP) is proposed from the viewpoint of non-convex optimization in an artificial potential field (APF). In ACPP, the initial path is the trajectory of the optimization process from the initial point to the target one. In a complex environment, the drivable area is divided by the edges into many relatively isolated regions, which leads to a non-convex path planning problem. Therefore, the clustering based on path nodes is set adaptively to make the potential function of each isolated region convex in the potential field. In each isolated region, the environment can be perceived by updating the parameters with sampling based on probability distribution functions, and these functions have similar influence to the cost function in optimization. Inspired by convex optimization, the quasi-line search algorithm is proposed for sampling points. Therefore, ACPP reduces the number of samples, dramatically, and has advantages of both sampling-based and APF-based algorithms in path planning. Based on the line segment based map which can be obtained from sensors readily, a gridding strategy is used to further reduce the time complexity. A series of simulation and experiential results validate the effectiveness of ACPP in virtual and real-world environments, respectively.
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