Meta-heuristic Algorithms for Solving The 3D Volume Coverage Problem Using Multi-UAVs

Abstract

Unmanned Aerial Vehicle (UAV) technology has been adopted rapidly across many applications such as surveillance, search and rescue missions, remote sensing, 3D-maps reconstruction, wireless coverage, and many other applications. Multiple UAVs are used more frequently than a single UAV, as they perform more complex tasks with better efficiency and in less time. Most of UAV-based applications depend on solving the 3D volume coverage problem, such as wireless coverage and 3D-maps reconstruction. The solution of the volume coverage problem highly depends on the proper deployment and placement of the UAVs in a 3D space, to cover the volume of interest. In this paper, the 3D volume coverage problem is solved using various meta-heuristic algorithms as Genetic Algorithm (GA), hybrid Genetic - Whale optimization algorithm (GA-WOA) and Binary Multi-Verse Optimization algorithm (BMVO). The solution of this problem is a hybrid one, in which the number of deployed UAVs is minimized, while covering a maximum volume and minimizing the overlap between separate volumes covered by the UAVs. The meta-heuristic algorithms are developed and tested using MATLAB/SIMULINK. To compare the results of the three implemented algorithms, two case studies are implemented to test the performance of the developed algorithms in aspects of Normalized Volume Covered (NVC), number of UAVs used, CPU running time and standard deviation of average cost function. The hybrid GA-WOA algorithm showed a higher coverage capability than GA and BMVO with 4.711% and 5.78%, but the GA showed better usage of UAVs than GA & BMVO by 30.44% and 40.57% respectively. For the CPU time, BMVO showed a faster convergence than GA and GA-WOA, by 75.34% and 81.66% respectively, with relatively good volume coverage and UAVs usage.