R3R: Decentralized Multi-Agent Collision Avoidance with Infinite-Horizon Safety

2025-10-07

AI Summary

Multi-agent systems under limited communication range lack formal infinite-horizon safety guarantees. Method: This paper proposes the first decentralized asynchronous motion planning framework that provides rigorous collision-avoidance safety for nonlinear Dubins-type agents operating over dynamic topologies. It integrates Guardian-based safety control with R-bounded geometric constraints to establish, for the first time, a theoretical linkage between communication radius and distributed safety planning capability. Forward invariance analysis and locally informed asynchronous trajectory optimization ensure infinite-horizon safety using only neighbor-to-neighbor communication. Results: In high-density simulations with 128 agents, the framework achieves 100% collision-free operation, with safety performance invariant to system scale—demonstrating both formally provable safety and computationally scalable decentralized planning.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Existing decentralized methods for multi-agent motion planning lack formal, infinite-horizon safety guarantees, especially for communication-constrained systems. We present R3R, to our knowledge the first decentralized and asynchronous framework for multi-agent motion planning under distance-based communication constraints with infinite-horizon safety guarantees for systems of nonlinear agents. R3R's novelty lies in combining our gatekeeper safety framework with a geometric constraint called R-Boundedness, which together establish a formal link between an agent's communication radius and its ability to plan safely. We constrain trajectories to within a fixed planning radius that is a function of the agent's communication radius, which enables trajectories to be shown provably safe for all time, using only local information. Our algorithm is fully asynchronous, and ensures the forward invariance of these guarantees even in time-varying networks where agents asynchronously join, leave, and replan. We validate our approach in simulations of up to 128 Dubins vehicles, demonstrating 100% safety in dense, obstacle rich scenarios. Our results demonstrate that R3R's performance scales with agent density rather than problem size, providing a practical solution for scalable and provably safe multi-agent systems.

Problem

• Achieving infinite-horizon safety in decentralized multi-agent collision avoidance
• Ensuring safety under communication constraints for nonlinear agent systems
• Providing scalable safety guarantees in asynchronous time-varying networks

Innovation

• Decentralized framework with infinite-horizon safety guarantees
• Combines gatekeeper safety with geometric R-Boundedness constraint
• Ensures safety using local information within communication radius