The Scenario-Based Model Predictive Control (SB-MPC) (SB-MPC) is an autonomous collision avoidance algorithm primarily designed for open and coastal waters. Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one One of the challenges in adapting SB-MPC for autonomous inland waterway collision avoidance is the inherent resolution insufficiency due inherent resolution insufficiency due inherent resolution insufficiency due inability to deriving solutions from deriving solutions from deriving solutions from use a finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based derivative based optimization strategy would be required. However, the would be required. However, the would be required. However, the due to non-smooth components of the SB-MPC of the SB-MPC of the SB-MPC in its cost function prohibit this approach. Therefore, function prohibit this approach. Therefore, function prohibit this approach. Therefore, function. Hence, we propose a newer variant of the newer variant of the newer variant of the novel algorithm, Smooth Scenario-Based Model Predictive Control (Smooth-SBMPC), (Smooth-SBMPC), (Smooth-SBMPC), (Smooth-SBMPC) specifically designed for the the the highly constrained and complex navigational environments inherent to inland waterways. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. The effectiveness of Smooth-SBMPC is validated through a comprehensive simulation study, offering offering offering providing insights into its performance in complex navigational environments.