Abstract

The Scenario-Based Model Predictive Control (SB-MPC) is an autonomous collision avoidance algorithm primarily designed for open and coastal waters. One of the challenges in adapting SB-MPC for autonomous inland waterway collision avoidance is the inability to use a derivative based optimization strategy due to non-smooth components in its cost function. Hence, we propose a novel algorithm, Smooth Scenario-Based Model Predictive Control (Smooth-SBMPC) specifically designed for highly constrained and complex navigational environments inherent to inland waterways. The effectiveness of Smooth-SBMPC is validated through a comprehensive simulation study, providing insights into its performance in complex navigational environments.