A Novel Hybrid Path Planning Method for Sweep Coverage of Multiple Uavs

Abstract

Sweep coverage (SC) is an NP-hard problem that needs to be solved with a small number of mobile sensors (sensor nodes) to monitor or observe targets in wireless sensor networks (WSNs) over a period of time. One of the proposed solutions to optimize the lifetime of WSNs is to monitor targets in the area of interest (AoI) by path planning of the sensor nodes. Considering the applications of unmanned aerial vehicles (UAVs) in military and civilian fields, in this study, we model multiple UAVs to solve the SC problem based on non-uniformly distributed targets in the AoI. In this modeling, we conduct effective coverage path planning for UAVs by performing an SC task for targets in the AoI with multiple asynchronously flying UAVs. To this end, we propose a new approach, which we call Weighted Targets Sweep Coverage, based on the metaheuristic Crow Search Algorithm and the heuristic Greedy algorithm. In our proposed approach, we define a new objective function by considering the UAV task completion time and the AoI coverage rate as metrics. In the Monte Carlo simulations that we perform to measure the performance, we run 5 different scenarios considering the number of targets defined for AoI and the alpha parameters defined for the objective function. The results of the scenario studies show that the proposed approach outperforms the other algorithms by up to 21% and 50% in terms of UAV task completion time and AoI coverage rate, respectively.