COLREGs-aware Trajectory Optimization for Autonomous Surface Vessels

Journal article (2022)

Authors

A. Tsolakis Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
D. Benders Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
O.M. de Groot Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
V. Reppa Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
L. Ferranti Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 A. Tsolakis, D. Benders, O.M. de Groot, R.R. Negenborn, V. Reppa, L. Ferranti
DOI
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https://doi.org/10.1016/j.ifacol.2022.10.441
Model Predictive Control Autonomous Surface Vessels Traffic Regulations
More Info
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Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

This paper presents a rule-compliant trajectory optimization method for the guidance and control of autonomous surface vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea - known as COLREGs - relevant for motion planning. We use these traffic rules to derive a trajectory optimization algorithm that guarantees safe navigation in mixed-traffic conditions, that is, in traffic environments with human operated vessels. The choice of an optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system's evolution over a sufficiently long receding horizon. The ability to plan considering different types of constraints over a long horizon in a unified manner leads to a proactive motion planner that mimics rule-compliant maneuvering behavior. The efficacy of the derived algorithm is validated in different simulation scenarios.