Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals

Conference paper (2022)

Authors

J.M. de Vries Student
E. Trevisan Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
T. Das Student
B.F. Ferreira de Brito Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 J.M. de Vries, E. Trevisan, J. van der Toorn, T. Das, B.F. Ferreira de Brito, J. Alonso-Mora
DOI
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https://doi.org/10.1109/ICRA46639.2022.9811608
Cost function Automation Dynamics Regulation Predictive models Irrigation Sea surface
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive contouring control (LMPCC) to generate motion plans satisfying kino-dynamic and collision constraints in real-time while including regulation awareness. To incorporate regulations in the planning stage, we propose a cost function encouraging compliance with rules describing interactions with other vessels similar to COLlision avoidance REGulations at sea (COLREGs). These regulations are essential to make an ASV behave in a predictable and socially compliant manner with regard to other vessels. We compare the framework against baseline methods and show more effective regulation-compliant avoidance of moving obstacles with our motion planner. Additionally, we present experimental results in an outdoor environment.