基于粒子群优化和模糊逻辑的输电线路运维检修无人机自主路径规划与避障技术研究

该文研究一种基于粒子群优化(PSO)与模糊逻辑控制(FLC)相结合的无人机路径规划与动态障碍物避让系统。通过设计模糊逻辑隶属函数和规则,并利用PSO优化这些参数,以提高无人机在复杂动态环境中的路径规划能力。在实验中,设置静态和动态障碍物,并将目标位置定义为特定坐标。实验结果表明,该系统能够有效地规划路径并避开障碍物,具备较高的路径规划精确度和避障性能。这为无人机在复杂环境中的自主飞行提供有价值的技术支持。

This paper studies a path planning and dynamic obstacle avoidance system for unmanned aerial vehicles based on the combination of particle swarm optimization(PSO)and fuzzy logic control(FLC).By designing fuzzy logic membership functions and rules,and using PSO to optimize these parameters,the path planning ability of the UAV in complex dynamic environments can be improved.In the experiment,static and dynamic obstacles were set up,and the target position was defined as specific coordinates.Experimental results show that the system can effectively plan paths and avoid obstacles,and has high path planning accuracy and obstacle avoidance performance.This provides valuable technical support for the autonomous flight of UAVs in complex environments.