A Variable Prediction Horizon MPC Approach for Leader-Follower Transportation in Presence of Obstacles

Cooperative transportation with multi-robot systems can be effectively managed using a leader-follower Model Predictive Control (MPC) approach, especially in cluttered environments. In particular, accurate modeling enhances robustness in obstacle avoidance but increases computational demand, limiting real-world applicability. Thus, this work proposes a nonlinear MPC-based leader-follower strategy for cooperative transportation in static and dynamic obstacle environments, leveraging a fully actuated robot model. To manage computational cost, a state machine dynamically switches between two (short and long) prediction horizons based on obstacles proximity. The effectiveness of the outlined approach is assessed through Monte Carlo analysis in a numerical simulation, comparing it to a state-of-the-art method across various scenarios.