障碍物环境下的多UAV自适应目标搜索算法研究
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摘要
针对未知环境下,无人飞行器(UAV,Unmanned Aerial Vehicle)利用传统搜索模式或离线设计航迹规划的方法进行多目标搜索,尤其是在未知的障碍物环境中,利用传统的搜索算法会出现搜索效率低,定位精度不稳定和环境适应能力差等问题。文章提出一种多UAV自适应目标搜索方法,根据传感器获取环境的目标信息,利用分区域协同规划策略,实现多UAV对所分配的子区域做自适应目标搜索;仿真结果表明,该方法能够很好地完成在未知环境中出现不确定障碍物情况下的目标搜索任务,而且在保障搜索到的目标状态信息可信度的情况下,具有较强的环境适应性,搜索效率较高。
Aiming at the unknown environment, the Unmanned Aerial Vehicle(UAV) uses the traditional search mode or offline design route planning method to carry out multi-objective search, especially in the unknown obstacle environment. The use of traditional search algorithms will lead to low search efficiency. The positioning accuracy is unstable and the environmental adaptability is poor. In this paper, a multi-UAV adaptive target search method is proposed, in which the target information of the environment is obtained by the sensor and the sub-regional cooperative planning strategy is used to realize the multi-UAV adaptive target search for the assigned sub-region. The simulation results show that, this method can well complete the target search task in the case of uncertain obstacles in the unknown environment, and in the case of ensuring the credibility of the target state information, it has strong environmental adaptability and high search efficiency.
Aiming at the unknown environment, the Unmanned Aerial Vehicle(UAV) uses the traditional search mode or offline design route planning method to carry out multi-objective search, especially in the unknown obstacle environment. The use of traditional search algorithms will lead to low search efficiency. The positioning accuracy is unstable and the environmental adaptability is poor. In this paper, a multi-UAV adaptive target search method is proposed, in which the target information of the environment is obtained by the sensor and the sub-regional cooperative planning strategy is used to realize the multi-UAV adaptive target search for the assigned sub-region. The simulation results show that, this method can well complete the target search task in the case of uncertain obstacles in the unknown environment, and in the case of ensuring the credibility of the target state information, it has strong environmental adaptability and high search efficiency.
引文
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[2]周建军,陈趋,崔麦金.无人机的发展及其军事应用[J].航空科学技术,2003,1:38-40.
[3]L.Bertuccelli,J.How,Search for dynamic targets with uncertain probability maps,in:Proc.of the American Control Conference,ACC 2006,p.6.
[4]F.Bourgault,T.Furukawa,H.F.Durrant-Whyte,Optimal search for a lost target in a bayesian world,in:Field and Service Robotics,FSR 2003,pp.209-222.
[5]Z.Mathews,S.B.i Badia,P.F.Verschure,Pasar:an integrated model of prediction,anticipation,sensation,attention and response for artificial sensorimotor systems,Inf.Sci.186(2012)1-19.
[6]J.F.Ferreira,J.Dias,Probabilistic approaches to robotic perception,Springer Tracts in Advanced Robotics,vol.91,Springer,2014.
[7]J.Tisdale,Z.Skim,H.Karl,et al.,"Autonomous UAV path planning and estimation,"IEEE Robotics&Automation Magazine,vol.16,no.2,pp.35-42,2009.