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Record W7064397588

Autonomous and safe capture of large space debris with a robotic manipulator

2015· dissertation· en· W7064397588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsSpace debrisRendezvousAerospaceSpacecraftTrajectorySatelliteMechatronicsRobotic armManipulator (device)
DOInot available

Abstract

fetched live from OpenAlex

Today's space industry faces a great challenge: the proliferation of space debris.With already more than twenty thousand traceable objects in low Earth orbit, from small fragments of only ten centimetres in size to large defunct satellites or used rocket engines, many fear an unstoppable cascade of collisions known as the Kessler syndrome.To regain control on the population of satellites orbiting the Earth, active debris removal and on-orbit servicing of satellites have imposed themselves as necessary technologies.Unfortunately, their technology readiness levels are still very low, clouding the future with uncertainty.A promising solution to both active debris removal and on-orbit servicing is the use of a robotic manipulator on-board a servicer spacecraft to rendezvous with and capture objects targeted for disposal or servicing.Deploying a space manipulator in proximity to a tumbling, non-cooperative satellite and manoeuvring to capture it while preserving the integrity of both the target and the servicer is the challenging problem that this thesis addresses.The two main sub-problems studied in this thesis are the tracking and prediction of the target's tumbling motion, and the planning required to find a suitable collision-free trajectory that can achieve the capture in a timely fashion.The treatment of those problems are oriented towards the specific characteristics of an experimental test-bed at the Aerospace Mechatronics Laboratory of McGill University composed of a neutrally-buoyant airship as the free-floating target and the operation of an industrial manipulator on a linear track as the space manipulator analog.This thesis introduces a new family of Kalman filters that are most suitable for attitude estimation and prediction of a free-floating object.The filters are developed on formal mathematical notions from abstract algebra, geometric calculus, invariance principles, and discrete mechanics, which result in many desirable theoretical, numerical and practical properties.These properties enable the derivation of important extensions of the filters such as a steady-state formulation of the attitude estimator and a hybrid augmented iii I would like to express my deepest gratitude to my supervisor, Prof. Inna Sharf, who guided me through this long journey through her support, confidence in me, and her valuable input throughout.I could hardly imagine completing this thesis, in due time, without her.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.235
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it