用改进粒子群算法规划多旋翼编队飞行路径，避障且保持队形。
Path Planning for Multi-Copter UAV Formation Employing a Generalized Particle Swarm Optimization
- 提出广义粒子群优化算法（GEPSO）协同规划编队路径
- 仿真与实验验证算法可生成可行飞行路径并避开障碍物
- 适合需要多机协同执行复杂任务的无人机应用场景
本文研究多旋翼无人飞行器（UAV）在复杂空间中以编队形式探测周围表面的路径规划问题。将编队中心路径规划建模为联合目标代价函数。提出广义粒子群优化算法（GEPSO），用于生成可飞行、避障且满足任务要求的最优路径。进一步设计路径生成方案，为每架无人机分配维持编队构型的位置路径。通过仿真、对比实验和实际测试验证了所提方法的可行性与有效性。
原文摘要 · Abstract (English)
The paper investigates the problem of path planning techniques for multi-copter uncrewed aerial vehicles (UAV) cooperation in a formation shape to examine surrounding surfaces. We first describe the problem as a joint objective cost for planning a path of the formation centroid working in a complicated space. The path planning algorithm, named the generalized particle swarm optimization algorithm, is then presented to construct an optimal, flyable path while avoiding obstacles and ensuring the flying mission requirements. A path-development scheme is then incorporated to generate a relevant path for each drone to maintain its position in the formation configuration. Simulation, comparison, and experiments have been conducted to verify the proposed approach. Results show the feasibility of the proposed path-planning algorithm with GEPSO.
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。