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

TL;DR

This work tackles safe motion planning for fully actuated autonomous surface vessels (ASVs) navigating narrow inland waterways where static and quasi-static obstacles complicate path planning. It introduces an MPC-CBF framework that couples a model-predictive planner with High Order Control Barrier Functions and an adaptive obstacle inflation radius $r_o(\alpha_o)$ to reduce conservativeness while preserving safety, including rotated-ellipse obstacle representations $\frac{(x'_i)^2}{a_{mi}^2}+\frac{(y'_i)^2}{b_{mi}^2}\le1$ and $a_{mi}=a_{oi}+r_{oi}(\alpha_{oi})$, $b_{mi}=b_{oi}+r_{oi}(\alpha_{oi})$. The approach computes $r_o(\alpha_o)=(w|\sin\alpha_o|+l|\cos\alpha_o|)/2$ from geometry between the ASV and obstacles, inflates safety constraints, and uses a deadlock-recovery CLF term $V=(r+k_v(\psi-\psi_d))^2$ to safely rotate out of tight spots. Validation through simulations and real-world pool experiments demonstrates real-time performance at $10\,\mathrm{Hz}$ and successful navigation through narrow gaps with safety guarantees, highlighting practical impact for inland-waterway ASV deployment.

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.

Figures (7)

• Figure 1: The ASV with known areas for the waterway (white) and land (gray), a reference path (blue), and unknown floating obstacles (purple).
• Figure 2: ASV footprint with enclosing radius $r_{\text{max}}$ (blue), and inner radius $r_\text{min}$ (red).
• Figure 3: Comparison between a) MPC under a fixed enclosing circle, b) MPC with a set of circles, c) the proposed MPC-CBF framework. Under narrow circumstances, the fixed inflation radius fails. The set of circles can solve for narrow corridors, but fails when more obstacles require more complex maneuverability. The proposed framework can solve for these cases.
• Figure 4: Examples of the dynamic radius inflation. The ASV footprint is shown in black, the red ellipse represents the obstacle plus a safety distance, and the blue circles represent the considered inflation.
• Figure 5: Experimental setup illustration.

Theorems & Definitions (4)

• Definition 1
• Theorem 1
• Remark 1
• Remark 2