多无人机系统三维避障机动无碰撞编队控制中的群体智能
Swarm Intelligence in Collision-free Formation Control for Multi-UAV Systems with 3D Obstacle Avoidance Maneuvers
AI总结：
本研究受罗非鱼和鸽子集体行为启发，提出一种结合集中式与分布式控制器的多无人机无碰撞编队控制框架，并扩展至三维避障机动，在二维和三维场景中验证了其有效性。
摘要：
多智能体系统操控的最新进展表明，城市地区对多无人机系统实施的需求日益增长，而这些区域总是存在静态和动态障碍物。本研究的重点是引入一种受自然启发的无碰撞控制方法，用于考虑避障机动的多无人机系统。受罗非鱼和鸽子集体行为的启发，本研究提出的框架采用集中式控制器进行最优编队控制/恢复，该控制器由概率Lloyd算法定义，同时采用分布式控制器进行飞行器间碰撞和障碍物规避。此外，所提出的框架已扩展到具有三维机动的三维空间。最后，将该框架应用于二维和三维场景下的多无人机系统，所得结果表明了该方法在存在建筑物和不同类型障碍物情况下的有效性。
英文摘要：
Recent advances in multi-agent systems manipulation have demonstrated a rising demand for the implementation of multi-UAV systems in urban areas which are always subjected to the presence of static and dynamic obstacles. The focus of the presented research is on the introduction of a nature-inspired collision-free control for a multi-UAV system considering obstacle avoidance maneuvers. Inspired by the collective behavior of tilapia fish and pigeon, the presented framework in this study uses a centralized controller for the optimal formation control/recovery, which is defined by probabilistic Lloyd's algorithm, while it uses a distributed controller for the intervehicle collision and obstacle avoidance. Further, the presented framework has been extended to the 3D space with 3D maneuvers. Finally, the presented framework has been applied to a multi-UAV system in 2D and 3D scenarios, and obtained results demonstrated the validity of the presented method in the presence of buildings and different types of obstacles.
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