Multi-UAV Path Planning for Simultaneous Task Performing in Environment with Obstacles
Weixian Zhang, Shuai Mi, Jintao Chen · 2025
Some applications of unmanned airborne vehicle (UAV) swarms require multiple vehicles to be engaged in one task simultaneously. When there are many such tasks at separate locations in a field with obstacles, an algorithm is demanded to plan a path for each UAV to travel among task sites. In this paper we propose a multi-UAV path planning algorithm framework for simultaneous task performing. This framework leverages Euclidean shortest path (ESP) algorithm and genetic algorithm (GA) to optimize the total time consumption. The feasibility and effectiveness of the proposed framework is validated by numerical experiments. Besides, the performances of different GA chromosome encodings and crossover operators are tested, and the numerical results show that two-chromosome encoding with partially mapped crossover operator is the best choice in this problem.