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基于电鳗电导向导航行为和地图预处理的双向跳点搜索路径规划算法

Bidirectional Jump Point Search Path-Planning Algorithm Based on Electricity-Guided Navigation Behavior of Electric Eels and Map Preprocessing.

作者信息

Gong Hao, Tan Xiangquan, Wu Qingwen, Li Jiaxin, Chu Yongzhi, Jiang Aimin, Han Hasiaoqier, Zhang Kai

机构信息

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

Research Center for Materials and Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Biomimetics (Basel). 2023 Aug 25;8(5):387. doi: 10.3390/biomimetics8050387.

DOI:10.3390/biomimetics8050387
PMID:37754138
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10526936/
Abstract

The electric eel has an organ made up of hundreds of electrocytes, which is called the electric organ. This organ is used to sense and detect weak electric field signals. By sensing electric field signals, the electric eel can identify changes in their surroundings, detect potential prey or other electric eels, and use it for navigation and orientation. Path-finding algorithms are currently facing optimality challenges such as the shortest path, shortest time, and minimum memory overhead. In order to improve the search performance of a traditional A* algorithm, this paper proposes a bidirectional jump point search algorithm (BJPS+) based on the electricity-guided navigation behavior of electric eels and map preprocessing. Firstly, a heuristic strategy based on the electrically induced navigation behavior of electric eels is proposed to speed up the node search. Secondly, an improved jump point search strategy is proposed to reduce the complexity of jump point screening. Then, a new map preprocessing strategy is proposed to construct the relationship between map nodes. Finally, path planning is performed based on the processed map information. In addition, a rewiring strategy is proposed to reduce the number of path inflection points and path length. The simulation results show that the proposed BJPS+ algorithm can generate optimal paths quickly and with less search time when the map is known.

摘要

电鳗有一个由数百个电细胞组成的器官，称为电器官。这个器官用于感知和检测微弱的电场信号。通过感知电场信号，电鳗可以识别周围环境的变化，检测潜在猎物或其他电鳗，并将其用于导航和定位。路径寻找算法目前面临着最优性挑战，如最短路径、最短时间和最小内存开销。为了提高传统A*算法的搜索性能，本文提出了一种基于电鳗电引导导航行为和地图预处理的双向跳点搜索算法（BJPS+）。首先，提出了一种基于电鳗电诱导导航行为的启发式策略，以加速节点搜索。其次，提出了一种改进的跳点搜索策略，以降低跳点筛选的复杂度。然后，提出了一种新的地图预处理策略，以构建地图节点之间的关系。最后，基于处理后的地图信息进行路径规划。此外，还提出了一种重新布线策略，以减少路径拐点数量和路径长度。仿真结果表明，所提出的BJPS+算法在已知地图时能够快速生成最优路径，且搜索时间较短。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fd5/10526936/cf9c4b18f76a/biomimetics-08-00387-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fd5/10526936/38946fd7a80d/biomimetics-08-00387-g006.jpg
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本文引用的文献

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Improved A* Path Planning Method Based on the Grid Map.基于网格地图的改进A*路径规划方法
Sensors (Basel). 2022 Aug 18;22(16):6198. doi: 10.3390/s22166198.
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