This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness functions that not only ensure the formation but also include constraints for optimal and safe UAV operation. To optimize the fitness function and obtain a suboptimal path, we employ the teaching-learning-based optimization algorithm and then further enhance it with mechanisms such as mutation, elite strategy, and multi-subject combination. A number of simulations and experiments have been conducted to evaluate the proposed method. The results demonstrate that the algorithm successfully generates valid paths for the UAVs to fly in a triangular formation for an inspection task.

翻译：本研究针对无人机集群在任务执行过程中保持期望编队的路径规划问题。我们将该问题构建为优化任务，通过定义一组适应度函数，不仅确保编队保持，还包含无人机最优与安全运行的约束条件。为优化适应度函数并获得次优路径，我们采用基于教与学优化算法，并通过变异、精英策略及多主体组合等机制对其进行增强。通过大量仿真与实验对所提方法进行评估，结果表明该算法成功为无人机集群生成有效路径，使其能够以三角编队执行巡检任务。