A Global–Local Collaborative and Decomposition-Based Multiobjective Evolutionary Optimization Method for UAV 3-D Path Planning

In the context of the widespread application of unmanned aerial vehicles (UAVs) across various industries, effective path planning in three-dimensional (3-D) environments has emerged as a crucial challenge in their deployment. In real-world applications, UAV path planning missions are usually converted into multi-objective tasks and solved using evolutionary computation, where the optimal flight path should consider both the overall flight route length and potential terrain threat. However, the existing methods usually treat complete paths as individuals, and this modeling approach lacks the evaluation of track points and is unable to fully reflect the quality of the path. In addition, as the quantity of track points increases, it is difficult for the traditional genetic crossover operator to quickly converge to the global optimum in complex high dimensional objective space. Thus, in this paper, we propose a UAV 3-D path planning method utilizing the global-local collaborative modeling approach with a decomposition-based method (P2GLCM). In the P2GLCM method, the global objective functions and the local objective functions are used to evaluate the path and track points, respectively, to achieve accurate modeling. In addition, to efficiently utilize the high-quality track points in the candidate paths, a dominance relationship approach is introduced to guide the generation of offsprings in a point-by-point manner, improving the search capability in complex objective space. The experimental results on 3-D environments with unified representation of voxels demonstrate that P2GLCM outperforms current methods in convergence and effectiveness.