无人机集群协同搜索研究综述

The research review on UAV swarm cooperative search

无人机集群协同区域搜索能够有效地获取任务区域地面信息,降低环境不确定度。基于区域划分、机群均衡分配以及启发式算法的传统集群协同区域搜索方法依赖于事前设计规则且计算量大,属于不可生成规则算法。考虑任务环境不确定性,算法须满足快速性、智能性和鲁棒性,基于涌现理论的无人机集群协同搜索方法因信息融合能力强、具有高度的智能性而被采用。演化学习算法和强化学习算法是涌现理论中主要组成部分,这两类算法可根据不同的环境和任务生成新的集群行为规则。将系统分析和总结当前无人机集群协同搜索方法研究现状和进展,并据此指出现有研究中的不足以及未来的发展方向。

The cooperative region search of UAV swarm can obtain ground information of the mission region and reduce the uncertainty of environmental information effectively.The traditional collaborative region search methods based on the balanced allocation of divided region and the heuristic algorithms depend on the pre⁃designed rules and heavy computation,and have no ability to generate new rules of the cooperative search.These algorithms belong to the algorithms that can not evolve new rules.Due to the complexity of the mission environment,the algorithms must contain fast,intelligent and robust characteristics,the cooperative searching algorithms of UAV swarm based on emerging theory with strong information fusion ability,self⁃learning ability have been widely concerned.Evolutionary and reinforcement learning algorithms are the important parts of the emerging theory and both of them can generate some new cooperative searching rules according to the different environ⁃ment and task.The paper would systematically analyze and summarize the current research status and progress of cooperative search methods.Finally,the shortcomings of the existing research and the further development are put forward.