Provably Safe Decentralized Contingency MPC under State-Only Information and Limited Sensing for Nonlinear Multi-agent Systems
Proposes a state-based decentralized contingency MPC ensuring safety and convergence with limited sensing in multi-agent systems.
Key Findings
Methodology
This paper introduces a state-dependent safe set update mechanism integrated into a contingency MPC framework, enabling decentralized multi-agent collision avoidance under limited sensing. Each agent constructs local safe regions (safe sets) based solely on current state measurements, avoiding neighbor trajectory reconstruction. The safe sets are dynamically updated using a novel outer envelope approach, reducing conservatism and supporting plug-and-play operation. The control scheme combines Lyapunov-based stability guarantees with safety constraints, ensuring recursive feasibility and convergence in nonlinear dynamics. The approach supports real-time implementation with finite sensing ranges and memory-free interactions, facilitating scalable multi-agent coordination.
Key Results
- Simulations in dense vehicle intersection scenarios with 50 agents demonstrated collision-free trajectories, minimum inter-vehicle distance of 1.026 meters, well above the collision threshold of 0.8 meters. The method maintained recursive feasibility and convergence despite dynamic agent entry and exit, with no communication required. The safety set outer envelope effectively managed limited sensing ranges (6.5 meters), confirming robustness. Compared to traditional methods, the approach reduced conservatism and computational load, enabling real-time operation.
- The framework effectively handled nonlinear vehicle kinematic models, diverse maneuvers, and dynamic environments. It achieved high safety and efficiency, with all agents reaching their destinations without collisions, validating the scalability and robustness of the safety set construction. The method outperformed baseline approaches in terms of safety margins, computational efficiency, and plug-and-play capability.
- By eliminating dependency on neighbor trajectory history, the approach significantly enhances decentralization and real-time adaptability. It demonstrates strong potential for autonomous driving, drone formations, and robotic swarms, especially in scenarios with limited sensing and unreliable communication, paving the way for safer, more scalable multi-agent systems.
Significance
This research addresses a fundamental challenge in multi-agent control—ensuring safety without relying on communication or full state information. Its innovative safety set outer envelope and state-dependent updates enable scalable, robust collision avoidance in complex, nonlinear environments. The framework's theoretical guarantees and practical validation mark a significant step toward autonomous systems capable of safe, decentralized operation in real-world scenarios. It opens avenues for deploying large-scale autonomous vehicle fleets, drone swarms, and robotic teams in urban, industrial, and military applications, where sensing and communication are often limited or unreliable.
Technical Contribution
The paper introduces a novel safety set construction based on current state measurements, avoiding neighbor trajectory reconstruction. It integrates this with a contingency MPC that guarantees recursive feasibility, collision avoidance, and Lyapunov stability. The outer envelope of safety sets allows for limited sensing and plug-and-play operation, reducing conservatism and computational complexity. Theoretical proofs establish system stability and safety, extending MPC guarantees to nonlinear, decentralized multi-agent systems with finite sensing ranges. This work bridges the gap between reactive geometric methods and predictive control, providing a rigorous, scalable solution.
Novelty
This is the first work to develop a state-dependent safe set update mechanism that supports decentralized contingency MPC under limited sensing, without neighbor trajectory history. Unlike prior methods relying on trajectory prediction or communication, this approach leverages safety set outer envelopes based solely on current states, enabling plug-and-play operation. Its integration with Lyapunov-based guarantees for nonlinear systems marks a significant innovation, broadening the applicability of predictive safety control in real-world, sensing-constrained environments.
Limitations
- The approach assumes perfect model knowledge; external disturbances and model uncertainties are not considered, which could impact safety guarantees in practice.
- Measurement errors and sensing inaccuracies may affect safe set construction, potentially reducing robustness.
- Computational complexity may increase in extremely dense or highly dynamic environments, requiring further optimization for real-time scalability.
Future Work
Future research will focus on incorporating robustness to disturbances and sensing errors, possibly through adaptive or learning-based safety set updates. Extending the framework to heterogeneous agents and higher-dimensional dynamics, such as aerial vehicles, is also promising. Additionally, integrating learning mechanisms to adapt safety sets in changing environments could further enhance scalability and resilience, paving the way for widespread deployment in complex, real-world scenarios.
AI Executive Summary
This paper introduces a groundbreaking decentralized contingency MPC framework that ensures safety and convergence in multi-agent systems operating under limited sensing conditions. Traditional multi-agent control strategies often depend on neighbor trajectory sharing or communication, which can be unreliable or infeasible in real-world environments. To address this, the authors propose a novel approach where each agent constructs a local safe set based solely on its current state, eliminating the need for historical neighbor data.
The core innovation lies in the safe set outer envelope, which provides a conservative yet flexible representation of the safe region. This outer envelope enables agents to interact safely within finite sensing ranges, supporting plug-and-play operations where agents can dynamically join or leave without reconfiguring the entire system. The control scheme combines this geometric safety mechanism with Lyapunov functions, guaranteeing recursive feasibility, collision avoidance, and asymptotic convergence even in complex nonlinear dynamics.
Extensive simulations in a dense vehicle intersection scenario demonstrate the effectiveness of the approach. Fifty vehicles successfully navigate the intersection without collisions, maintaining a minimum inter-vehicle distance of 1.026 meters, far exceeding the collision threshold. The method adapts seamlessly to dynamic agent entry and exit, with no communication required, highlighting its scalability and robustness.
Overall, this work advances the state-of-the-art in decentralized multi-agent control by providing a theoretically sound, practically feasible solution for safe, autonomous operation under sensing limitations. Its potential applications span autonomous driving, drone swarms, and robotic teams, promising safer and more reliable multi-agent systems in complex environments. Future directions include robustness enhancements, scalability improvements, and adaptation to heterogeneous agents, aiming to realize fully autonomous, resilient multi-agent networks.
Deep Dive
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.