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Energy-Efficient UAVs Coverage Path Planning Approach

Computer Modeling in Engineering & Sciences 2023年第9期136卷 3239-3263页

作者： Gamil Ahmed Tarek Sheltami Ashraf Mahmoud Ansar Yasar Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility *** play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance *** path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large ***,energy consumption is one of the most challenging issues that need to be *** this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP *** objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole *** do so,the flight path is optimized and the number of turns is reduced to minimize the energy *** proposed approach first decomposes the ROI into a set of cells depending on a UAV camera ***,the coverage path planning problem is formulated,where the exact solution is determined using the cplex *** small-scale problems,the cplex shows a better solution in a reasonable ***,the cplex solver fails to generate the solution within a reasonable time for large-scale ***,to solve the model for large-scale problems,simulated annealing forCPP is *** results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the cplex ***,we compare the simulated annealing against the greedy *** results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

关键词： Coverage path planning MILP cplex solver energy model optimization region of interest area of interest

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