Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
It addresses the challenge of decentralized collision avoidance for nonlinear multi-agent systems with limited communication, providing theoretical guarantees that are often missing in prior work.
This paper presents a decentralized contingency MPC framework for nonlinear multi-agent systems that guarantees collision avoidance and recursive feasibility without inter-agent communication, using only state information. Simulations demonstrate effectiveness in sparse and dense environments, including cluttered bottlenecks and plug-and-play scenarios.
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.