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Record W2967682328 · doi:10.1109/icuas.2019.8798130

Onboard Generation of Optimal Flight Trajectory for Delivery of Fragile Packages

2019· article· en· W2967682328 on OpenAlex
Weihong Yuan, Luís Rodrigues

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsConcordia University
Fundersnot available
KeywordsTrajectoryTrajectory optimizationComputer scienceDroneConstraint (computer-aided design)Point (geometry)Optimal controlDynamic programmingBoundary (topology)Mathematical optimizationControl theory (sociology)SimulationEngineeringControl (management)AlgorithmMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Real-time onboard flight trajectory generation is of great importance for all kinds of flying vehicles. This paper proposes a method to generate the trajectory which minimizes the damage due to flight motion to a fragile package. The proposed methodology has several potential applications including drone organ delivery. A similar procedure can also be used in applications where the objective is to maximize passenger comfort during flight. An analytical solution of the optimal trajectory generation problem is derived under arbitrary two-point boundary value constraints. An approach to solve for the optimal flight time is also proposed, which can be easily implemented on common embedded processors. The algorithm is extended to guarantee that a peak velocity constraint is verified. The effects of a parameter called the cost index on the optimal solution are also discussed. Examples show how the procedure can be used in a specific application.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.500
Threshold uncertainty score0.277

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 head0.030
GPT teacher head0.246
Teacher spread0.216 · 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

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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