Provably Safe Decentralized Contingency MPC under State-Only Information and Limited Sensing for Nonlinear Multi-agent Systems
2026-08-31 • Multiagent Systems
Multiagent SystemsRobotics
AI summaryⓘ
The authors study how multiple agents can safely control themselves when they only know their own state and can sense nearby agents with limited range. They create a method that helps each agent plan safe fallback moves if needed, ensuring safety and stability without relying on detailed neighbor information. Their approach reduces unnecessary caution in how agents interact, making the system more flexible and fully decentralized. Simulations show their method works well even when there are many agents close together.
decentralized controlcontingency model predictive controlmulti-agent systemsfallback regionssafety guaranteesLyapunov stabilitylocal sensingplug-and-play operationrecursive feasibilityfinite sensing range
Authors
Max Studt, Georg Schildbach
Abstract
This paper considers decentralized contingency MPC for multi-agent control under a state-only information pattern, with particular focus on limited sensing and plug-and-play operation. The objective is to retain recursive feasibility, safety, and Lyapunov-type convergence while reducing conservatism in local interaction handling. The framework relies on agent-wise fallback regions (safe sets) in which a feasible contingency maneuver to a safe equilibrium is always available. A novel safe-set update mechanism is introduced that supports less conservative decentralized interaction while preserving the underlying guarantees. This, in turn, enables memory-free local interaction and finite sensing ranges without requiring agents to reconstruct the exact neighbor geometry. The resulting scheme remains fully decentralized and preserves the shared-first-input contingency MPC structure. Theoretical guarantees and simulation results illustrate the effectiveness of the approach in dense multi-agent scenarios.