A Novel Ant Colony-inspired Coverage Path Planning for Internet of Drones

Abstract

Coverage Path Planning (CPP) is a fundamental problem widely used in various applications such as monitoring, patrol, mapping, and sweeping. Several types of robots can perform CPP, which has become very popular with the use of multiple drones. The Internet of Drones (IoD) is an emerging technology that aims to facilitate a multi-drone environment for various applications. In this context, coordination and organization of drones are crucial to avoid collisions and congestion in the airspace. One way to achieve this is by using well-defined airways. CPP tasks are often performed using multiple drones to cover a given area, which can be integrated into the IoD system. However, existing CPP solutions for drones do not fully consider collaboration capabilities between them, specific energy constraints, and the dynamic IoD scenario. To address these challenges, this work proposes an Ant Colony Optimization Algorithm for CPP in IoD (AntIoD), considering energy consumption. This scenario is particularly challenging due to the unique characteristics of being a mix of CPP for terrestrial vehicles with road constraints and CPP for drone networks. We compared our approach, AntIoD, with three recent solutions from the literature. The results reveal that AntIoD outperformed them in all simulated scenarios by at least 10% in terms of energy efficiency when compared to the best scenarios of the other algorithms.