面向多无人机系统的新型鸽群启发式三维障碍检测与规避机动
Novel Pigeon-inspired 3D Obstacle Detection and Avoidance Maneuver for Multi-UAV Systems
AI总结：
受罗非鱼和鸽子群体行为启发，提出一种半分布式多无人机无碰撞编队控制框架，结合概率Lloyd's算法实现最优定位与三维障碍规避，并在二维和三维动态环境中验证了有效性。
摘要：
多智能体系统操控领域的最新进展表明，城市环境中对多无人机系统部署的需求日益增长，而城市环境始终存在静态和动态障碍物。受罗非鱼和鸽子群体行为的启发，本研究重点提出一种受自然启发的多无人机系统无碰撞编队控制方法，并考虑障碍规避机动。本研究开发的框架采用半分布式控制方法，其中基于概率Lloyd's算法，集中式引导算法负责无人机的最优定位，而分布式控制方法用于飞行器间碰撞避免和障碍规避。此外，所提出的框架通过三维机动的新定义扩展到三维空间。最后，将所提出的框架应用于二维和三维场景中的多无人机系统，所得结果验证了该方法在具有静止和移动障碍物的动态环境中的有效性。
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. Inspired by the collective behavior of tilapia fish and pigeons, the focus of the presented research is on the introduction of a nature-inspired collision-free formation control for a multi-UAV system, considering the obstacle avoidance maneuvers. The developed framework in this study utilizes a semi-distributed control approach, in which, based on a probabilistic Lloyd's algorithm, a centralized guidance algorithm works for optimal positioning of the UAVs, while a distributed control approach has been used for the intervehicle collision and obstacle avoidance. Further, the presented framework has been extended to the 3D space with a novel definition of 3D maneuvers. Finally, the presented framework has been applied to multi-UAV systems in 2D and 3D scenarios, and the obtained results demonstrated the validity of the presented method in dynamic environments with stationary and moving obstacles.
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。
