基于二维网格模型的移动机器人路径规划平滑跳跃点搜索算法

J. Robotics Pub Date : 2022-08-31 DOI:10.1155/2022/7682201
Zhenyu Yang, Junli Li, Liwei Yang, Hejiang Chen
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引用次数: 2

摘要

摘要针对传统A∗算法求解路径扩展节点多、内存开销大、运算效率低、路径角多的问题,将跳点搜索策略与自适应圆弧优化策略相结合,对传统A∗算法进行了改进。首先,为了提高我们路径的安全性,我们扩大了障碍物的风险区域。然后,将A∗算法与跳跃点搜索策略相结合,实现子节点跳跃搜索,减少了计算规模和内存开销,提高了搜索效率。考虑到障碍物密度对搜索效率的影响,根据障碍物密度的特殊效应对启发式函数进行了增强。最后,采用冗余跳点和自适应圆弧优化策略进一步缩短路径长度,提高初始路径的平滑度。仿真结果表明,该算法在路径长度、安全性和平滑性方面优于传统的A *和文献算法,并在大规模海洋环境和现实环境中得到进一步验证和应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model
To address the problems of the traditional A ∗ algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A ∗ algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A ∗ algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A ∗ and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.
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