考虑搜索收益平衡的多无人机运动目标协同搜索方法

Collaborative search method for moving targets with multiple unmanned aerial vehicles considering the balance of search revenue

中文关键词  无人机  协同搜索  滚动规划  云台扫摆拍照

英文关键词  unmanned aerial vehicles  collaborative search  rolling planning  gimbal sweep photography

中文摘要

      针对无人机集群协同搜索特定区域内多个运动目标问题, 考虑无人机群的飞行约束、传感器的探测概率与虚警概率、云台扫摆拍照探测等特征, 提出基于搜索收益平衡的多无人机协同搜索动目标方法. 以无人机集群搜索的短期收益、长期收益和协调收益的平衡为核心, 设计只涉及目标存在概率和环境不确定度, 高效率动态更新的搜索信息图, 建立了多无人机协同搜索数学规划模型, 并基于滚动规划架构和路径剪枝策略进行模型求解. 典型无人机集群协同搜索的数值算例验证了本文方法的有效性. 数值仿真结果表明, 本方法可以搜索到更多的目标且具有更少的误判次数, 有效提升多无人机协同搜索效能. 而且只采用目标存在概率和环境不确定度来构建搜索信息图, 避免了繁琐的计算和参数设置, 能高效更新环境信息, 有效引导无人机捕获更多的目标, 并在秒级的时间内做出每架无人机航迹决策.

英文摘要

      Aiming at the problem of multi-moving target search in a specific area by unmanned aerial vehicle (UAV) clusters, a multi-UAV cooperative searching method based on search revenue balance is proposed, considering the flight constraints of UAV, detection probability and false alarm probability of sensors, and the detection characteristics of gimbal sweep photography. Taking the balance of short-term benefits, long-term benefits and coordination benefits of UAV clusters search as the core, a search infographic with high efficiency and dynamic update involving only the probability of target existence and environmental uncertainty is designed, and a mathematical programming model for multi-UAV collaborative search is established, and the model is solved based on the rolling planning architecture and path pruning strategy. The effectiveness of the proposed method is verified by a numerical example of collaborative search of typical UAV clusters. Numerical simulation results show that this method can search more targets and has less misjudgment times, and effectively improve the efficiency of multi-UAV collaborative search. Moreover, only the probability of target existence and environmental uncertainty are used to construct the search information map, which avoids tedious calculation and parameter setting, and can effectively update the environmental information, effectively guide the UAV to capture more targets, and make the track decision of each UAV within seconds.