发表机构
Alma Mater Studiorum - Università di Bologna; Leonardo S.p.a.; Leonardo Innovation Labs（博洛尼亚大学; 莱昂纳多公司; 莱昂纳多创新实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对杂乱环境中无人机群追踪动态目标的计算与安全挑战，研究提出MROPE分层策略，结合分布式聚合优化、分布式共识方案与预测安全滤波器，通过动态聚合障碍物为椭圆保障安全，实验验证其效率与可扩展性优于集中式方法。
AI 中文摘要
在杂乱环境中部署无人机群追踪动态目标面临严峻的计算与安全挑战。我们提出MROPE，一种分层策略，将协同监测任务与严格的局部安全要求解耦。为克服密集空间中典型的计算瓶颈，我们的方法为每架无人机动态聚合复杂障碍物几何形状为单个安全边界椭圆。该架构通过三部分实现：用于高层群协同的分布式聚合优化、用于安全区域计算的分布式共识方案、用于实时避障的局部预测安全滤波器（PSF）。虚拟与真实世界实验验证了该框架，与集中式方法相比，展现出更优的实时效率与可扩展性。
英文摘要
Deploying drone swarms to track a dynamic target in cluttered environments presents severe computational and safety challenges. We propose MROPE, a hierarchical strategy that decouples the cooperative monitoring mission from strict local safety requirements. To overcome the computational bottlenecks typical of dense spaces, our approach dynamically aggregates complex obstacle geometries into a single safe bounding ellipse for each drone. Methodologically, this architecture is realized by combining distributed aggregative optimization for high-level swarm coordination, a decentralized consensus scheme for the safe area computation, and local Predictive Safety Filters (PSF) for real-time collision avoidance. Virtual and real-world experiments validate the framework, demonstrating superior real-time efficiency and scalability compared to centralized approaches.
