计算机科学
算法
运动规划
移动机器人
平滑度
边界(拓扑)
路径(计算)
区间(图论)
点(几何)
人工智能
节点(物理)
机器人
Suurballe算法
搜索算法
欧几里德距离
数学优化
数学
Dijkstra算法
最短路径问题
图形
数学分析
几何学
结构工程
理论计算机科学
组合数学
工程类
程序设计语言
作者
Xing Xu,Jiazhu Zeng,Yun Zhao,Xiaoshu Lü
标识
DOI:10.1016/j.eswa.2023.122922
摘要
In order to shorten searching time and reduce the quantity of redundant nodes in path planning, an improved A* algorithm was proposed. In the novel algorithm, compared with the A* algorithm, the octet neighborhood was replaced a rectangular boundary without obstacles. The map was explored in two directions from the starting point and the target point respectively. The exploration generated fewer nodes. Due to the enlargement of the search neighborhood and the rectilinear passage without obstacles in the rectangular region, the new algorithm used Euclidean distance as distance estimate. And a new operator was designed to seek the best search node to reduce the time complexity, so as to improve the efficiency of the algorithm. For improving the safety of mobile robot, the novel algorithm adopted adaptive cost function to improve the ability of road safety discrimination. A Slide-Rail corner adjustment method was designed to reduce unnecessary corners and improve the smoothness of the path. The simulation results showed that, compared with the traditional A* algorithm and various improved A* algorithms, the proposed algorithm can shorten the path length and searching time, reduce the number of turns, the total turning angles and search nodes. Compared to A*, the searching time was reduced by 64.76%, the total turning angles were reduced by 56.34%, and the search nodes were reduced by 82.76% averagely in test maps of this paper. Moreover, the average interval width in improved A* was 3.07 and was more than 1.5 times the average interval width in Rectangle Expansion A*.
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