基于编队教-学优化算法的无人机集群路径规划
Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization
AI总结：
针对无人机集群作业时保持编队的路径规划问题，提出结合变异、精英策略等机制的增强型教-学优化算法，可生成有效路径支撑三角编队巡检任务。
摘要：
本研究针对一组无人机（UAV）在作业过程中保持期望编队的路径规划问题展开研究。我们的方法通过定义一组适应度函数将该问题转化为优化任务，这些函数不仅能保障编队形态，还纳入了无人机最优且安全运行的约束条件。为优化适应度函数并获得次优路径，我们采用了教-学优化算法，进一步通过变异、精英策略、多主体组合等机制对其进行增强。我们开展了大量仿真与实验以评估所提方法。结果表明，该算法能成功生成有效路径，使无人机以三角编队执行巡检任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
