Open Access

ARTICLE

Energy-Efficient UAVs Coverage Path Planning Approach

Gamil Ahmed¹, Tarek Sheltami^1,*, Ashraf Mahmoud¹, Ansar Yasar²

1 Computer Engineering Department, Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
2 Transportation Research Institute (IMOB), Hasselt University, Hasselt, 3500, Belgium

* Corresponding Author: Tarek Sheltami. Email:

(This article belongs to this Special Issue: Digital Twins in Smart Transportation and Mobility)

Computer Modeling in Engineering & Sciences 2023, 136(3), 3239-3263. https://doi.org/10.32604/cmes.2023.022860

Received 29 March 2022; Accepted 26 October 2022; Issue published 09 March 2023

Abstract

Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage 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 region. Therefore, energy consumption is one of the most challenging issues that need to be optimized. In this paper, we propose an energy-efficient coverage path planning algorithm to solve the CPP problem. The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region. To do so, the flight path is optimized and the number of turns is reduced to minimize the energy consumption. The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint. Then, the coverage path planning problem is formulated, where the exact solution is determined using the CPLEX solver. For small-scale problems, the CPLEX shows a better solution in a reasonable time. However, the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems. Thus, to solve the model for large-scale problems, simulated annealing for CPP is developed. The results show that heuristic approaches yield a better solution for large-scale problems within a much shorter execution time than the CPLEX solver. Finally, we compare the simulated annealing against the greedy algorithm. The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

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Keywords

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