用改进教学-学习算法规划无人机群队形飞行路径
Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization
- 基于改进教学-学习算法优化路径，兼顾队形保持与安全
- 仿真验证可生成三角队形飞行的有效路径
- 适合无人机编队巡检等需要稳定队形的任务
本文针对无人机群在执行任务时保持期望队形的路径规划问题，将问题建模为优化任务，定义了一组包含队形约束和安全飞行条件的适应度函数。采用改进的教学-学习优化算法求解，并引入变异、精英保留和多主体组合机制以提升性能。通过大量仿真与实验评估，结果表明该算法能成功生成无人机以三角队形完成巡检任务的有效飞行路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。