JACIII Vol.24 No.7 pp. 934-943
doi: 10.20965/jaciii.2020.p0934


A Heuristic Lowest Unknown-Degree Target Search Strategy Under Non-Structured Environment for Multi-Agent Systems

Chunye Wang*,** and Chen Chen*,**,†

*School of Automation, Beijing Institute of Technology
5 South Zhongguancun Street, Haidian District, Beijing 100081, China

**State Key Laboratory of Intelligent Control and Decision of Complex System
Beijing 100081, China

Corresponding author

October 20, 2020
November 9, 2020
December 20, 2020
non-structural, unknown environment, lowest unknown-degree, multi-target search

Multi-target searching is a hotspot and foundation topic in multi-agent systems research. However, most of the research is based on simple environment or known environment, which greatly limits the application of target search. In the non-structured environment, the searching result can be greatly affected by the complex terrain constraints and detectability of targets especially when we have no prior knowledge about the environment. In the paper, a novel search strategy combining maximum visibility and particle swarm optimization is proposed for the target search problem in a completely unknown and non-structural environment. The strategy utilizes the concept of visibility to describe how well the agent detects the map, and guides the agent to perform online path planning to complete the search task. In addition, considering the limited communication distance and communication bandwidth, the strategy introduces a cooperative mechanism for each agent to improve the search efficiency. Finally, in the experimental part, the search strategy is compared with the commonly used search strategies. Compared with the methods combining advantages, the proposed strategy can still achieve similar results, which proves the feasibility and efficiency of the strategy.

Cite this article as:
Chunye Wang and Chen Chen, “A Heuristic Lowest Unknown-Degree Target Search Strategy Under Non-Structured Environment for Multi-Agent Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 934-943, 2020.
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Last updated on Mar. 01, 2021