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Record W2809555803 · doi:10.1109/lra.2018.2849553

Local Positioning System Using UWB Range Measurements for an Unmanned Blimp

2018· article· en· W2809555803 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

VenueIEEE Robotics and Automation Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRobustness (evolution)Computer scienceEstimatorKalman filterControl theory (sociology)Extended Kalman filterGyroscopeSimulationReal-time computingEngineeringArtificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

Unmanned blimps are a safe and reliable alternative to conventional drones when flying above people. On-board real-time tracking of their pose and velocities is a necessary step toward autonomous navigation. There is a need for an easily deployable technology that is able to accurately and robustly estimate the pose and velocities of a blimp in 6 DOF, as well as unexpected applied forces and torques, in an uncontrolled environment. We present two multiplicative extended Kalman filters using ultrawideband radio sensors and a gyroscope to address this challenge. One filter is updated using a dynamics model of the blimp, whereas the other uses a constant speed model. We describe a set of experiments in which these estimators have been implemented on an embedded flight controller. They were tested and compared in accuracy and robustness in a hardware-in-loop simulation as well as on a real blimp. This approach can be generalized to any lighter than air robot to track it with the necessary accuracy, precision, and robustness to allow autonomous navigation.

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: none
Teacher disagreement score0.710
Threshold uncertainty score0.592

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 science0.0000.000
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 head0.031
GPT teacher head0.233
Teacher spread0.203 · 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