Path Planning of UAV Using Levy Pelican Optimization Algorithm In Mountain Environment

Abstract

To overcome the issues of simple strategy and easy fall into local optimum when solving UAV 3D path planning problem, we propose an improved Levy Pelican Optimization Algorithm (LPOA) that incorporates the hunter-prey algorithm and solves the UAV 3D path planning problem based on the improved LPOA. Based on the original algorithm, on the one hand, we introduce Levy flight and adaptive parameters to update the pelican position, balance the algorithm development phase and exploration phase, and effectively avoid the problem of falling into local optimum at the early stage of the algorithm. On the other hand, we use chaotic mapping to initialize the population, integrate the prey generation strategy to enhance the prey generation formula and improve algorithm orientation and convergence speed. The test results are compared and analyzed with those of classical, current mainstream, and recently released swarm intelligence optimization algorithms. Using 3D mountain range model experiments, the algorithm is compared before and after optimization, and the improved optimization algorithm is then applied to the actual problem of UAV 3D path planning. Research has found that the improved LPOA optimizes the total target cost of the algorithm by 9.75% and optimizes the planned average shortest path by 20.30%.