A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions

Abstract

Multi-UAV path planning algorithms are crucial for the successful design and operation of unmanned aerial vehicle (UAV) networks. While many network researchers have proposed UAV path planning algorithms to improve system performance, an in-depth review of multi-UAV path planning has not been fully explored yet. The purpose of this study is to survey, classify, and compare the existing multi-UAV path planning algorithms proposed in the literature over the last eight years in various scenarios. After detailing classification, we compare various multi-UAV path planning algorithms based on time consumption, computational cost, complexity, convergence speed, and adaptability. We also examine multi-UAV path planning approaches, including metaheuristic, classical, heuristic, machine learning, and hybrid methods. Finally, we identify several open research problems for further investigation. More research is required to design smart path planning algorithms that can re-plan pathways on the fly in real complex scenarios. Therefore, this study aims to provide insight into the multi-UAV path planning algorithms for network researchers and engineers to contribute further to the design of next-generation UAV systems.