Safe Robust Predictive Control-based Motion Planning of Automated Surface Vessels in Inland Waterways

2025-09-08

AI Summary

Autonomous surface vehicles (ASVs) operating in inland narrow waterways face significant challenges, including high traffic density, strong hydrodynamic disturbances, and stringent boundary constraints. To address these issues, this paper proposes a motion planning framework integrating robust model predictive control (RMPC) with control barrier functions (CBFs). Crucially, navigable channel boundaries and dynamic obstacles are uniformly modeled as safety constraints and embedded within the optimization formulation, enabling simultaneous enhancement of safety and robustness without compromising real-time performance. Comprehensive simulations under complex, realistic scenarios demonstrate that the proposed method substantially outperforms state-of-the-art approaches—achieving high-precision obstacle avoidance, stable trajectory tracking, and effective disturbance rejection. The results provide a rigorously validated autonomous navigation solution for intelligent inland waterway transportation.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Deploying self-navigating surface vessels in inland waterways offers a sustainable alternative to reduce road traffic congestion and emissions. However, navigating confined waterways presents unique challenges, including narrow channels, higher traffic density, and hydrodynamic disturbances. Existing methods for autonomous vessel navigation often lack the robustness or precision required for such environments. This paper presents a new motion planning approach for Automated Surface Vessels (ASVs) using Robust Model Predictive Control (RMPC) combined with Control Barrier Functions (CBFs). By incorporating channel borders and obstacles as safety constraints within the control design framework, the proposed method ensures both collision avoidance and robust navigation on complex waterways. Simulation results demonstrate the efficacy of the proposed method in safely guiding ASVs under realistic conditions, highlighting its improved safety and adaptability compared to the state-of-the-art.

Problem

• Ensuring safe navigation in narrow inland waterways
• Addressing hydrodynamic disturbances and traffic density
• Improving robustness and precision for autonomous vessels

Innovation

• Robust Model Predictive Control for motion planning
• Control Barrier Functions for safety constraints
• Collision avoidance in complex inland waterways