The persistent surveillance problem has been proved to be an NP hard problem for multiple unmanned aerial vehicle systems (UAVs). However, most studies in multiple UAV control focus on control cooperative path planning in a single swarm, while dynamic deployment of a multiswarm system is neglected. This paper proposes a collective control scheme to drive a multiswarm UAVs system to spread out over a time-sensible environment to provide persistent adaptive sensor coverage in event-related surveillance scenarios. We design the digital turf model to approximate the mixture information of mission requirements and surveillance reward. Moreover, we design a data clustering-based algorithm for the dynamic assignment of UAV swarms, which can promote workload balance, while also allowing real-time response to emergencies. Finally, we evaluate the proposed architecture by means of simulation and find that our method is superior to the conventional control strategy in terms of detection efficiency and subswarm equilibrium degree.
In this paper, we present a three-layer centralized multiple unmanned aerial vehicles(UAVs) task assignment method in collaboration with satellites for forest fire reconnaissance task, based on Gaussian mixture model (GMM) and multidimensional 0-1 knapsack model. The goal of UAVs reconnaissance task assignment problem is to maximize the utilization of UAVs by the reasonable allocation of UAV group. Hence, it is taken as a combinatorial optimization problem in this paper. We first obtain the basic information of the fire field through the space-based infrared sensors carried by satellites. Then we fit the fire points distribution through expectation maximization algorithm (EM) based on GMM to obtain the initial position of each UAV. Finally, we consider the task assignment problem of UAVs as a multidimensional 0-1 knapsack problem and use the improved genetic algorithm (GA) inspired by the fireworks algorithm (FWA) with improved select operator and elite opposition-based learning strategy (SeEl-FWGA) to solve the UAVs task assignment problem. Finally, the proposed method is demonstrated and compared with other methods under the background of forest fires in Liangshan Prefecture, Sichuan Province. The simulation results show the superiority to the traditional genetic algorithm in terms of iteration speed and iteration results, and the high efficiency to solve the UAVs task assignment problem.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.