Safe Motion Planning and Control Using Predictive and Adaptive Barrier Methods for Autonomous Surface Vessels

2025-10-01

AI Summary

Autonomous surface vessels (ASVs) face significant challenges in motion planning within narrow inland waterways, including high computational load, overly conservative obstacle avoidance, and susceptibility to deadlock. To address these issues, this paper proposes a safety-critical navigation framework integrating model predictive control (MPC) with adaptive high-order control barrier functions (CBFs). A key innovation is the introduction of time-varying, relative-pose-based elliptical obstacle representations, which substantially reduce conservatism inherent in conventional fixed-ellipsoidal models. By jointly enforcing adaptive obstacle inflation and high-order CBF constraints within the MPC optimization, the method achieves real-time, safe, and flexible closed-loop control. Comprehensive simulations and full-scale vessel experiments demonstrate that the approach enables efficient obstacle avoidance and proactive deadlock resolution in complex, confined waterways—while simultaneously enhancing trajectory flexibility and computational efficiency without compromising safety.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Adversarial Agents

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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combining Model Predictive Control (MPC) and Control Barrier Functions (CBFs). We introduce a time-varying inflated ellipse obstacle representation, where the inflation radius is adjusted depending on the relative position and attitude between the vessel and the obstacle. The proposed adaptive inflation reduces the conservativeness of the controller compared to traditional fixed-ellipsoid obstacle formulations. The MPC solution provides an approximate motion plan, and high-order CBFs ensure the vessel's safety using the varying inflation radius. Simulation and real-world experiments demonstrate that the proposed strategy enables the fully-actuated autonomous robot vessel to navigate through narrow spaces in real time and resolve potential deadlocks, all while ensuring safety.

Problem

• Safe motion planning for autonomous vessels in narrow waterways
• Reducing conservativeness in obstacle avoidance using adaptive inflation
• Real-time navigation through narrow spaces while ensuring safety

Innovation

• Combining Model Predictive Control with Control Barrier Functions
• Using time-varying inflated ellipse obstacle representation
• Adaptive inflation radius adjusted by relative position and attitude