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

TL;DR

This paper introduces a decentralized MPC approach for multi-agent collision avoidance that guarantees safety and convergence without requiring inter-agent communication, using safe sets and contingency planning.

Contribution

It develops a novel decentralized contingency MPC framework with a safe-set update mechanism ensuring recursive feasibility and collision avoidance in nonlinear multi-agent systems.

Findings

Guarantees recursive feasibility and collision avoidance.

Demonstrates safe convergence in simulations.

Operates effectively in dense and cluttered environments.

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.