Energy-Efficient Multi-UAV Multi-Region Coverage Path Planning Approach

Abstract

Due to the high deployment flexibility and strong maneuverability, unmanned aerial vehicles (UAVs) have gained a significant attention in civilian and military applications. One of the main essential aspects of UAV is coverage path planning (CPP), which autonomously obtains sufficient paths to cover the entire region of interest (RoI). Several advantages have been offered by UAVs’ CPP such as cost and time efficiency, reduced human intervention, resource optimization, data collection, scalability, and adaptability. However, the flight time of UAVs is constrained by battery capacity, necessitating energy-efficient solutions to prolong flight duration. This paper introduces a novel approach for energy-efficient multi-UAV multi-region CPP to generate appropriate paths that cover multiple disjoint regions, aiming to minimize overall energy consumption. First, we employ a back-and-forth strategy to generate intra-region path patterns with minimum turns and propose a smoothing turns approach (STA) based on Bezier curves to effectively reduce an energy consumption due to taking turns. Then, the inter-region path planning is formulated as a multi-constraint optimization problem and solved utilizing the CPLEX solver for small-scale problems and heuristic approaches for large-scale ones. A region allocation approach is proposed to assign RoIs to appropriate UAVs. Simulations are conducted to evaluate the performance of the proposed approach in terms of energy consumption. Comparative results against EECPPA and Nearest Neighbor (NN) approaches demonstrate advantages in energy consumption reduction. Besides, heuristic methods yield superior solutions for large-scale problems within shorter execution times compared to the CPLEX solver. These comparisons highlight the superiority of the proposed approach over existing methods in generating higher-quality solutions.