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STERN: Simultaneous Trajectory Estimation and Relative Navigation for Autonomous Underwater Proximity Operations
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Aerospace, moveability and naval architecture. (Underwater Robotics)ORCID iD: 0000-0003-2336-9401
Massachusetss Institue of Technology, Cambridge, MA 02142, USA.ORCID iD: 0009-0001-5655-1501
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-7796-1438
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Aerospace, moveability and naval architecture. (Underwater Robotics)ORCID iD: 0000-0002-1090-9168
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of relative navigation for the generalized version of the docking operation, which we address as proximity operations. Proximity operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We emphasize the versatility on the use of factor graphs as a generalized representation to model the underlying simultaneous trajectory estimation and relative navigation (STERN) problem that arises with any prox-ops scenario, regardless of the sensor suite or the agents' dynamic constraints. To emphasize the flexibility of factor graphs as the modeling foundation for arbitrary underwater prox-ops, we compile a list of state-of-the-art research in the field and represent the different scenario using the same factor graph representation. We detail the procedure required to model, design, and implement factor graph-based estimators by addressing a long-distance acoustic homing scenario of an AUV to a moving mothership using datasets from simulated and real-world deployments; an analysis of these results is provided to shed light on the flexibility and limitations of the dynamic assumptions of the moving target. A description of our front- and back-end is also presented together with a timing breakdown of all processes to show its potential deployment on a real-time system. 

National Category
Robotics and automation
Research subject
Computer Science; Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-372988OAI: oai:DiVA.org:kth-372988DiVA, id: diva2:2014171
Note

Accepted for publication in the IEEE-OES Journal of Oceanic Engineering (JOE) in 2025.

QC 20251117

Available from: 2025-11-17 Created: 2025-11-17 Last updated: 2025-11-17Bibliographically approved
In thesis
1. Relative Navigation for Autonomous Underwater Proximity Operations
Open this publication in new window or tab >>Relative Navigation for Autonomous Underwater Proximity Operations
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis dives into the world of autonomous underwater robotics, specifically oriented at solving the relative navigation problem that arises during an underwater multi-agent proximity operation (prox-op). The present document starts with an introduction to the background theoretical and practical concepts required for the reader to follow the contributions outlined within. We define the concept of proximity operations in the underwater domain and highlight a factor-graph-based robotic state estimation framework used to intuitively model arbitrary prox-ops as Simultaneous Trajectory Estimation and Relative Navigation (STERN) problems. We continue by outlining the attached scientific contributions which carefully address the different elements of the general factor graph representation in a procedural manner: we start by isolating the two navigation-dependent phases of the prox-op and solve them independently; subsequently, we study the full scenario from end to end. The document is redacted such that it provides the story in hindsight surrounding the scientific contributions that are part of this compilation thesis.

Abstract [sv]

Denna avhandling fördjupar sig i världen av autonoma undervattensrobotar, med särskilt fokus på att lösa det relativa navigationsproblemet som uppstår under en undervattensoperation med flera samverkande enheter i närhet (prox-op). Avhandlingen inleds med en introduktion till de teoretiska och praktiska bakgrundskoncept som krävs för att läsaren ska kunna följa de bidrag som presenteras.    Vi definierar begreppet närhetsoperationer inom undervattensdomänen och lyfter fram en faktorgradsbaserad ram för robotars tillståndsuppskattning, som används för att intuitivt modellera godtyckliga prox-ops. Denna ram kallas för Simultaneous Trajectory Estimation and Relative Navigation (STERN). Vi fortsätter med att redogöra för de vetenskapliga bidrag som ingår, vilka metodiskt behandlar de olika elementen i den allmänna faktorgrafsrepresentationen: vi börjar med att isolera de två navigationsberoende faserna i prox-op och löser dem var för sig; därefter studerar vi hela scenariot från början till slut. Avhandlingen är utformat för att i efterhand ge en berättelse kring de vetenskapliga bidragen som ingår i denna sammanläggningsavhandling.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2025. p. xxi, 67
Series
TRITA-SCI-FOU ; 2025:70
Keywords
autonomous underwater vehicles, state estimation, proximity operations, underwater navigation, Autonoma undervattensfarkoster, tillståndsuppskattning, närhetsoperationer, undervattensnavigering
National Category
Robotics and automation
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-372996 (URN)978-91-8106-485-8 (ISBN)
Public defence
2025-12-19, https://kth-se.zoom.us/j/67637947676, F3, Lindstedtsvägen 26, Stockholm, 10:00 (English)
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Supervisors
Note

QC 20251119

Available from: 2025-11-17 Created: 2025-11-17 Last updated: 2025-12-02Bibliographically approved

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Terán Espinoza, AldoFolkesson, JohnDeutsch, ClemensRolleberg, NiklasSigray, PeterKuttenkeuler, Jacob

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