未知城市环境下的多机协同目标搜索方法研究

Research on multi-UAV cooperative target search method under unknown urban environment

在城市环境中,建筑物或不可达区域等因素的影响易造成多无人机(unmanned aerial vehicle,UAV)协同路径规划策略失效,从而导致目标搜索任务的失败。针对此问题,提出未知城市环境下的多UAV协同搜索(multi-unmanned aerial vehicle cooperative search,MUCS)方法。首先,对城市环境进行建模,其中涵盖密集建筑物群的设计和运动状态多样的目标,以增强目标搜索任务的挑战性;然后,在此基础上,综合考虑UAV编队飞行约束和信息交互能力,构建基于信息共享代价和区域覆盖收益的协同优化模型;最后,根据多UAV协同编队特点,利用群智能方法进行优化求解,确保每架UAV均能得到最优路径可行解,从而提高多UAV协同目标搜索效率。与现有搜索方法相比,MUCS方法的平均目标发现成功率提升了20%,区域覆盖率提升了10%。实验结果表明,MUCS方法具有较强的目标搜索能力和区域覆盖能力。

In the urban environment,the influence of factors such as buildings or inaccessible regions is easy to cause the failure of the multi-unmanned aerial vehicle(UAV)cooperative path planning strategy,which results in the failure of the target search task.To solve these issues above,a multi-UAV cooperative search(MUCS)method in the uncertain urban environment is proposed.Firstly,the urban environment is modeled,which includes the design of dense building clusters and targets with various motion states,so as to enhance the challenge of the target search task.Then on this basis,a cooperative optimization model based on information sharing cost and regional coverage benefit is constructed by comprehensively considering the flight constraint and the information interaction capability of UAV formation.Finally,according to the characteristics of the multi-UAV cooperative formation,the swarm intelligence method is used to solve the optimization problem,which ensures that each UAV can obtain the feasible solution of the optimal path and improve the efficiency of the multi-UAV cooperative target search.Compared with the existing search methods,the average target discovery success rate of MUCS method is increased by 20%,and the regional coverage rate is increased by 10%.The experimental results illustrate that MUCS has the strong capability of the target search and regional coverage.