Fuzzy multiobjective cooperative surveillance of multiple UAVs based on distributed predictive control for unknown ground moving target in urban environment

Abstract

In this paper, a fuzzy multiobjective path planning method based on distributed predictive control is proposed to deal with the problem of cooperative searching and tracking of unknown ground moving target by multiple unmanned aerial vehicles (UAVs) in urban environment. Firstly, extended Kalman filter (EKF) is combined with probability estimation to predict the states of the unknown target. Secondly, the line of sight occlusion of buildings, and energy consumptions of UAVs and sensors are considered in path planning. The objective functions are designed as target coverage degree, control input cost of UAV and sensor energy consumption respectively. The cooperative surveillance path planning problem is transformed into multiobjective optimization with different importance levels. Thirdly, distributed predictive control is used to obtain the local optimal path of each UAV. The predictive states of UAVs in finite horizon are exchanged to build the collision avoidance constraint, and the minimum turning radius constraint is also addressed. Then, all the objectives are fuzzified to handle the different importance level requirement. The sensor energy consumption function with switch value is equivalently converted using Sigmoid function and sign function. According to the principle that the objective with higher priority has higher satisfactory degree, preemptive priorities are transformed into the relaxed order of satisfactory degrees. The best path satisfying the requirement of multiobjective optimization and importance levels can be obtained. Finally, the simulation results show the effectiveness of the proposed method by comparing with traditional multiobjective weighted algorithm.