MROPE: A Multi-Robot Safe Cooperative Strategy via combined Predictive Safety Filters and Ellipse-based Constraint Compression

2026-07-31

AI Summary

This work addresses the challenges of real-time performance and safe collision avoidance in multi-robot systems when tracking dynamic targets in complex environments. The authors propose a hierarchical cooperative framework wherein high-level coordination leverages distributed consensus optimization for scalable task allocation, while a low-level predictive safety filter (PSF) ensures local obstacle avoidance. A key innovation lies in dynamically aggregating multiple obstacles into a single safety ellipse, coupled with ellipse constraint compression to substantially reduce computational complexity. Experimental results demonstrate that the proposed approach outperforms centralized baselines in both simulated and real-world scenarios, achieving strict safety guarantees while significantly enhancing real-time responsiveness and system scalability.

Abstract

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.

Problem

• drone swarms
• dynamic target tracking
• cluttered environments
• computational challenges
• safety challenges

Innovation

• Predictive Safety Filters
• Ellipse-based Constraint Compression
• Distributed Aggregative Optimization
• Decentralized Consensus
• Multi-Robot Coordination