多无人机协同追踪中，用椭圆压缩障碍物提升安全与效率。
MROPE: A Multi-Robot Safe Cooperative Strategy via combined Predictive Safety Filters and Ellipse-based Constraint Compression
- 将复杂障碍物聚合成单个安全椭圆，降低计算负担。
- 实测显示相比中心化方法，响应速度更快、可扩展性更强。
- 适合需要实时避障的无人机集群任务场景。
在复杂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。本文提出MROPE，一种分层协同策略，将协同监测任务与局部安全约束解耦。为克服密集空间中的计算瓶颈，该方法为每架无人机动态聚合复杂障碍几何形状，生成单一安全包围椭圆。方法上结合分布式聚合优化实现高层群组协调、去中心化共识机制计算安全区域，以及局部预测安全滤波器（PSF）实现实时碰撞规避。虚拟与真实实验验证了该框架的有效性，结果表明其在实时效率和可扩展性方面优于集中式方法。
原文摘要 · Abstract (English)
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.
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