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Differentiable Programming for the Autonomous Movement Planning of a Small Vessel

Bahls, Christian and Schubert, Agnes (2021) Differentiable Programming for the Autonomous Movement Planning of a Small Vessel. TransNav : International Journal on Marine Navigation and Safety of Sea Transportation, 15 (3), pp. 493-499. Faculty of Navigation Gdynia Maritime University. doi: 10.12716/1001.15.03.01. ISSN 2083-6473.

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Official URL: https://www.transnav.eu/Article_Differentiable_Programming_for_the_Autonomous_Movement_Planning_of_a_Small_Vessel_Bahls,59,1141.html

Abstract

In this work we explore the use of differentiable programming to allow autonomous movement planning of a small vessel. We aim for an end to end architecture where the machine learning algorithm directly controls engine power and rudder movements of a simulated vessel to reach a defined goal. Differentiable programming is a novel machine learning paradigm, that allows to define a systems parameterized response to control commands in imperative computer code and to use automatic differentiation and analysis of the information flow from the controlling inputs and parameters to the resulting trajectory to compute derivatives to be used as search directions in an iterative algorithm to optimize a goal function. Initially the method does not know about any manoeuvring or the vessels response to control commands. The method autonomously learns the vessels behaviour from several simulation runs. Finally, we will show how the simulated vessel is able to fulfil some small missions, like crossing a flowing river while avoiding crossing traffic.

Item URL in elib:https://elib.dlr.de/145683/
Document Type:Article
Title:Differentiable Programming for the Autonomous Movement Planning of a Small Vessel
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bahls, ChristianUNSPECIFIEDhttps://orcid.org/0000-0003-0511-0017UNSPECIFIED
Schubert, AgnesUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:17 September 2021
Journal or Publication Title:TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:15
DOI:10.12716/1001.15.03.01
Page Range:pp. 493-499
Publisher:Faculty of Navigation Gdynia Maritime University
ISSN:2083-6473
Status:Published
Keywords:Autonomous Navigation, Autonomous Movement Planning, Small Vessel, Differentiable Programming, Autonomous Movement, Machine Learning Method, Machine Learning, Simulation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - I4Port (old)
Location: Neustrelitz
Institutes and Institutions:Institute of Communication and Navigation > Nautical Systems
Deposited By: Bahls, Dr. Christian
Deposited On:19 Nov 2021 11:14
Last Modified:05 Dec 2023 10:10

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