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Record W4381186310 · doi:10.11159/cdsr23.209

Estimation of Time-Varying Inertia of Aerial Manipulators Performing Manipulation of Unknown Objects

2023· article· en· W4381186310 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Conference of Control, Dynamic systems, and Robotics · 2023
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceInertiaArtificial intelligenceEstimationComputer visionControl theory (sociology)EngineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper presents a method for estimating the varying inertia parameters of unmanned aerial manipulators (UAMs) while they perform manipulation maneuvers on a priori unknown objects and execute various flight maneuvers.The varying mass and moment of inertia tensor of the UAM are estimated using the UAM's force and the kinematic model of its robotic arm by means of a novel sequential Kalman filter (KF) algorithm.In order to validate the effectiveness of the mechanism, it is implemented on a highlymaneuverable VTOL-type UAV equipped with a 3 degree-of-freedom (DoF) manipulator capable of moving only on the longitudinal plane of the aircraft.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.262
Teacher spread0.241 · 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