Multi-objective Path Planning of Multiple Unmanned Air Vehicles Using the CCMO Algorithm

The key to efficiently executing tasks with multiple unmanned air vehicles(UAVs) is effective path planning. The goal of path planning is to plan a path which is shorter, smoother, and has minimal fluctuations while avoiding collisions. In order to improve the quality of flight paths, this study utilized the Coevolutionary Constrained Multi-Objective Optimization (CCMO) algorithm for cooperative path planning of multiple UAVs. This algorithm consists of two populations, the main population which addresses the original problem and the auxiliary population which handles the unconstrained problem containing objective 1. Additionally, we simulated two mountain environments with different levels of complexity, each containing no-fly areas. Finally, we conducted path planning for multiple UAVs in above two environments and used cubic B-spline curves to smooth the generated paths. Experimental results demonstrate that the CCMO algorithm can better address the multiple UAVs path planning problem compared to other four contrastive algorithms.