采用广义粒子群优化的多旋翼无人机编队路径规划
Path Planning for Multi-Copter UAV Formation Employing a Generalized Particle Swarm Optimization
AI总结：
针对多旋翼无人机编队巡检的路径规划问题，提出GEPSO算法规划质心避障路径，并通过路径生成方案保持各机编队位置，仿真与实验验证了可行性。
摘要：
本文研究多旋翼无人机（UAV）以编队形态协同检查周围表面时的路径规划技术问题。我们首先将该问题描述为在复杂空间中为编队质心规划路径的联合目标代价。随后提出名为广义粒子群优化（GEPSO）的路径规划算法，用于构建一条最优且可飞行的路径，同时避开障碍物并确保满足飞行任务要求。接着引入一种路径生成方案，为每架无人机生成相应路径，以保持其在编队构型中的位置。已开展仿真、比较和实验来验证所提方法。结果表明，所提出的采用GEPSO的路径规划算法具有可行性。
英文摘要：
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.
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。
