Probabilistic Trajectory Generation Based on Distributed Model Predictive Control for Multi-Robot Systems

This paper proposes a probabilistic trajectory generation algorithm based on distributed model predictive control (MPC) for multi-robot systems in the presence of state estimation noises and motion disturbances. Our method enhances collision avoidance by incorporating uncertainties through the time-aware Safe Corridor (TASC) formulation. Considering the uncertainties, we establish collision avoidance chance constraints by transforming probabilistic conditions into deterministic constraints on the mean and covariance of robot states. To prevent potential deadlocks, we introduce a resolution strategy that combines a warning band with the right-hand rule. These chance constraints and the deadlock resolution strategy are integrated into the distributed MPC framework to generate locally optimal trajectories. Simulation results show that our approach significantly improves both safety and stability in uncertain environments.