Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals

Master thesis (2021)

Authors

J.M. de Vries Mechanical Engineering

Contributors

J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (mentor)

E. Trevisan Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (mentor)

Rens M. Doornbusch Amsterdam Institute for Advanced Metropolitan Solutions (AMS) (mentor)

B. Atasoy Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (graduation committee member)

Riccardo M.G. Ferrari Team Riccardo Ferrari - Mechanical, Maritime and Materials Engineering (graduation committee member)

Faculty

Mechanical Engineering, Mechanical Engineering

Model Predictive Control Motion Planning Regulations Urban Autonomous Surface Vessels Model Predictive Contouring Control Canals Local Planning

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Published Date

27-09-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

With a growing number of citizens and tourists, the scarce public space, roads, and public transport in Amsterdam are experiencing rising pressure. Instead of utilizing the conventional transportation routes, Autonomous Surface Vessels (ASVs) could transport goods and people via the 165 canals present in Amsterdam.
However, urban canals are challenging for motion planning since the space can be narrow and contain other human-operated boats.
Unfortunately, there are limited existing works regarding motion planning for ASVs in urban canals with dynamic obstacles. Additionally, motion planners for other applications can not be applied directly because of the lacking traffic structure in the canals, the specific dynamics of ASVs, and the regulations applying to canals.
Therefore, the purpose of this thesis is to develop a motion planning framework that can plan dynamically feasible motions for ASVs in urban canals complying with regulations. These rules are not only mandatory but adhering to them makes the ASV's motion socially compliant, and therefore, more predictable by other canal users.
We build upon local model predictive contouring control and extend it with regulation compliance. Adherence to rules is achieved by constructing a new cost function for the optimization problem that allocates a higher cost on specific sides of the obstacle vessel. With this new cost function, the Roboat can behave according to the regulations in takeovers, head-on encounters, and crossings with boats from port and starboard.
Furthermore, the effect of predicting the obstacle vessel trajectories with a Social variational recurrent neural network is compared to the constant velocity model.
The entire motion planning framework is compared in simulation with LMPCC and the current motion planning and control framework of the Roboat, breadth first search combined with Nonlinear Model Predictive tracking Control (NMPC).
Additionally, the motion planning framework is implemented on a quarter-scale Roboat and is tested in an outdoor environment with disturbances.

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