A 3D UAV Path Planning Method Based on Multi-Strategy Improved Artificial Rabbit Optimization Algorithm

The 3D UAV (Unmanned Aerial Vehicle) path planning problem aims to plan an optimal flight path for the UAV while satisfying safety conditions. In this paper, a cost function for UAV path planning is constructed by means of mathematical modeling, so that the UAV path planning problem is transformed into a multi-constrained optimization problem, and metaheuristic algorithms are applied to solve this problem. Aiming at the shortcomings of artificial rabbit optimization algorithm which is slow to converge and easy to fall into local optimum, this paper develops an improved Artificial Rabbit Optimization algorithm based on Levy flight, adaptive Cauchy mutation, and elite population Genetic strategy (LCGARO). Multifaceted comparison experiments are conducted between LCGARO and six classical and advanced heuristic algorithms in 29 CEC2017 test functions and six 3D UAV path-planning terrain scenarios of varying complexity. The results of the comparison experiments prove that the LCGARO algorithm proposed in this paper has better optimization accuracy among 22 test functions in the comparison experiments of CEC2017 test functions. In the UAV path planning experiments, the LCGARO algorithm is able to plan a flight path with the smallest total cost function value in five terrain scenarios. © 2024 Chinese Institute of Electronics. All rights reserved.