Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance

2026-05-11

AI Summary

This work addresses the challenges of decentralized collision avoidance for nonlinear multi-agent systems in the absence of trajectory information exchange, where existing approaches often suffer from excessive conservativeness and difficulties in guaranteeing recursive feasibility and convergence. The paper proposes a safety-set-based decentralized emergency model predictive control framework, wherein each agent relies solely on its local state and employs a consensus rule to couple nominal trajectories with emergency certificates to ensure collision avoidance. A novel geometric safety set update mechanism is introduced to guarantee recursive feasibility across consecutive time steps, complemented by a Lyapunov-like convergence theory that enables plug-and-play operation. The method demonstrates effective, robust, and scalable collision-free navigation in both sparse and dense environments, including those with complex bottlenecks.

Abstract

Decentralized collision avoidance remains challenging, particularly when agents do not communicate any information related to planned trajectories. Most existing approaches either rely on conservative coordination mechanisms or provide limited guarantees on recursive feasibility and convergence. This paper develops a decentralized contingency MPC framework for multi-agent systems with nonlinear dynamics that achieves collision-free motion under a state-only information pattern. Each agent follows the same consensual rule set, enabling safe decentralized planning without communication. Each agent solves a local optimization problem that couples a nominal trajectory with a contingency certificate ensuring a feasible backup maneuver under receding-horizon operation. A novel geometric and decentralized safe-set update mechanism prevents feasibility loss between consecutive time steps. The resulting scheme guarantees recursive feasibility, including collision avoidance, and establishes a Lyapunov-type convergence result to an admissible safe equilibrium. Simulation results demonstrate performance in both sparse and dense multi-agent environments, including cluttered bottleneck scenarios and under plug-and-play operation.

Problem

• Decentralized collision avoidance
• Multi-agent systems
• Nonlinear dynamics
• Recursive feasibility
• Safe sets

Innovation

• Decentralized MPC
• Safe Sets
• Collision Avoidance
• Nonlinear Multi-agent Systems
• Recursive Feasibility