An improved artificial bee colony algorithm for 3D UAV logistics path planning problem
LIU Run-kai
HU Wei
SONG Yan-jie
XING Li-ning
Abstract：Unmanned aerial vehicles(UAVs),with their air mobility and autonomy,have shown significant value in logistics and distribution.This model can significantly reduce manpower costs and improve the flexibility and response efficiency of the logistics network.However,obstacles such as buildings and mountains in three-dimensional complex environments pose serious challenges to UAV flight safety.How to construct the distribution path under the constraint of obstacle avoidance has become a key issue in the optimisation of UAV logistics system.For the demand of 3D path planning,this study establishes a comprehensive analysis framework containing environment modelling and mathematical modelling,and proposes an improved artificial bee colony algorithm incorporating a reinforcement learning mechanism.The algorithm adopts a heuristic rule based on the spatial relationship between the starting and finishing points to generate the initial population,and dynamically selects three search strategies in the honey bee stage through reinforcement learning,which significantly improves the quality of the initial solution and the directionality of the search.In the observation bee stage,a reverse learning mechanism is introduced to generate complementary populations to enhance the convergence accuracy and speed of the algorithm.Simulation experiments show that compared with the traditional algorithm,the improved algorithm has significant advantages in terms of path cost and computational efficiency,and can provide an efficient solution for UAV logistics path planning in complex 3D scenes.
Keywords：reinforcement learningartificial bee colony algorithmsdroneslogistics and distributionpath planning
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：12( 2274-2285 )