面向环境探测的多智能体自组织目标搜索算法

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Title:

Self-organizing target search algorithm of multi-agent system for envi-ronment detection

作者:

吴莹莹¹, 丁肇红¹, 刘华平^2,3, 赵怀林¹, 孙富春^2,3

1. 上海应用技术大学 电气与电子工程学院, 上海 201418;
2. 清华大学 计算机科学与技术系, 北京 100084;
3. 清华大学 智能技术与系统国家重点实验室, 北京 100084

Author(s):

WU Yingying¹, DING Zhaohong¹, LIU Huaping^2,3, ZHAO Huailin¹, SUN Fuchun^2,3

1. School of Electrical and Electronics Engineering, Shanghai Institute of Technology, Shanghai 201418, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. State Key Laboratory of Intelligent Techn

关键词:

自组织算法; 目标搜索; 差分进化; 仿生集群; 无人机; 非结构化环境; 鸟群效应; 动态目标

Keywords:

self-organizing algorithm; target search; differential evolution algorithm; multi-agent bionic algorithm; unmanned aerial vehicle; unstructured environment; flocking; dynamic target

分类号:

TP242.6

DOI:

10.11992/tis.201908023

摘要:

针对在复杂非结构化环境下如何协调多个无人机发现静态或动态目标的问题，建立了自组织目标搜索算法框架。结合磁探仪等效平均探测宽度模型，受昆虫协调方式和鸟群效应的生物机制启发，提出了基于仿生集群算法的无人机集群分布式目标搜索模型；采用改进的自适应差分进化算法帮助无人机集群模型在环境中平衡勘探和探索，实现无人机群体的协同搜索优化。该自组织目标搜索算法旨在以最短时间实现跟踪目标数量的最大化。基于仿真平台的实验测试了该策略的性能，验证了算法对具有未知目标的非结构化复杂环境的适用性。

Abstract:

In this study, the framework of self-organizing target search algorithm was developed to coordinate unmanned aerial vehicle (UAV) swarm to find static and dynamic targets in a complex unstructured environment. First, the UAVs distributed target search model was developed from the biologically-inspired mechanisms called flocking and stigmergy, which incorporated the magnetic detector’s equivalent average width feature. Secondly, an improved differential evolution algorithm, which introduced adaptive operators, was proposed for the balancing of exploration and exploitation in the multi-UAV collaborative search system and realizing optimization of UAV collaborative search. This self-organizing target search algorithm aims at optimizing the number of tracking targets in the shortest possible time. The target search strategy tested on the simulation framework validates the algorithm’s adaptability for uncertain spatial targets in unstructured complex scenarios.