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Relative Navigation and Dynamic Target Tracking for Autonomous Underwater Proximity Operations
AI, Robotics and Cybersecurity Center (ARC), Örebro University, Örebro, Sweden.ORCID iD: 0009-0001-0731-8754
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Aerospace, moveability and naval architecture. (Underwater Robotics)ORCID iD: 0000-0003-2336-9401
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, USA.ORCID iD: 0009-0001-5655-1501
AI, Robotics and Cybersecurity Center (ARC), Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-3122-693X
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Estimating a target’s 6-DoF motion in underwater proximity operations is difficult because the chaser lacks target-side proprioception and the available relative observations are sparse, noisy, and often partial (e.g., Ultra-Short Baseline (USBL) positions). Without a motion prior, factor-graph maximum a posteriori estimation is underconstrained: consecutive target states are weakly linked and orientation can drift.    We propose a generalized constant-twist motion prior defined on the tangent space of Lie groups that enforces temporally consistent trajectories across all degrees of freedom; in SE(3) it couples translation and rotation in the body frame. We present a ternary factor and derive its closed-form Jacobians based on standard Lie group operations, enabling drop-in use for trajectories on arbitrary Lie groups.    We evaluate two deployment modes: (A) an SE(3)-only representation that regularizes orientation even when only position is measured, and (B) a mode with boundary factors that switches the target representation between SE(3) and 3D position while applying the same generalized constant-twist prior across representation changes.    Validation on a real-world dynamic docking scenario dataset shows consistent ego-target trajectory estimation through USBL-only and optical relative measurement segments with an improved relative tracking accuracy compared to the noisy measurements to the target. Because the construction relies on standard Lie group primitives, it is portable across state manifolds and sensing modalities.

Keywords [en]
target tracking, state estimation, factor graphs, motion priors, proximity operations, autonomous docking, autonomous underwater vehicle navigation
National Category
Robotics and automation
Research subject
Vehicle and Maritime Engineering; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-372994OAI: oai:DiVA.org:kth-372994DiVA, id: diva2:2014176
Note

Submitted to the IEEE-OES Journal Of Oceanic Engineering in Sep. 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)
Opponent
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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, JohnSigray, PeterKuttenkeuler, Jacob

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