Solving the area coverage problem with UAVs: A vehicle routing with time windows variation

In real life, providing security for a set of large areas by covering the areas with Unmanned Aerial Vehicles (UAVs) is a difficult problem that consists of multiple objectives. These difficulties are even greater if the area coverage has to be sustained through a specific time window. We address th...

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Vydané v:Robotics and autonomous systems Ročník 126; s. 103435
Hlavní autori: Semiz, Fatih, Polat, Faruk
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.04.2020
Predmet:
Simplex algorithm
Transportation problem
Unmanned aerial vehicle
Vehicle routing problem
VRPTW
Transportation problem
Unmanned aerial vehicle
VRPTW
Vehicle routing problem
Simplex algorithm
ISSN:0921-8890, 1872-793X
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Popis
Shrnutí:In real life, providing security for a set of large areas by covering the areas with Unmanned Aerial Vehicles (UAVs) is a difficult problem that consists of multiple objectives. These difficulties are even greater if the area coverage has to be sustained through a specific time window. We address this by considering a Vehicle Routing Problem with a Time Windows (VRPTW) variation in which the capacity of agents is counted as one and each customer (target area) is to be supplied with more than one vehicle simultaneously and without violating time windows. In this problem, our aim is to find a way to cover all areas with the necessary number of UAVs during the time windows, while minimizing the total distance traveled, and providing a fast solution by satisfying the additional constraint that each agent has limited fuel. We present a novel algorithm that relies on clustering the target areas according to their time windows, and then incrementally generating transportation problems with each cluster and the ready UAVs. We then solve the transportation problems with a simplex algorithm. The performance of the proposed algorithm and other algorithms implemented in order to compare the solution quality is evaluated through example scenarios with practical problem sizes.
ISSN:0921-8890
1872-793X
DOI:10.1016/j.robot.2020.103435