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Record W1537667663

Planification de trajectoires pour une flotte d'UAVs

2010· article· fr· W1537667663 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.

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

VenuePhDT · 2010
Typearticle
Languagefr
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

RESUME Les drones, qu’on appelle aussi UAVs (Unmanned Aerial Vehicles) ou RPV (Remotely Piloted Vehicles), sont des avions sans pilote. Ils sont adoptes par des organisations militaires et civiles pour accomplir des tâches difficiles dans des environnements tres hostiles, sans aucun risque pour l’humain. Comme toute nation developpee, le Canada a recours a ces avions pour ses missions militaires de maintien de la paix et pour des missions civiles telles que la surveillance du littoral, la surveillance des grands territoires et pour l’aide lors d’operations de sauvetage. L’utilisation des UAVs est tres diverse, que ce soit pour des operations militaires ou civiles, et l’engouement pour ces engins est de plus en plus grand dans les pays industrialises. Les progres realises dans les technologies de controle, de detection et de calcul ont permis a ces vehicules de realiser des missions independantes du controle direct de l'operateur. L’itineraire peut etre planifie a l'avance de la mission et le drone peut alors l’executer automatiquement. La planification de cet itineraire efficace implique la determination de solutions permettant d’atteindre un certain but fixe, comme par exemple eviter que le drone soit detecte par les radars au cours de son itineraire, ou trouver l'itineraire le plus court en terme de temps de deplacement, ou encore la minimisation du cout de la mission.--------- ABSTRACT UAVs (Unmanned Aerial Vehicles) or RPV (Remotely Piloted Vehicles), are planes without pilots. They were adopted by military and civilian organizations to accomplish difficult tasks over hostile environments and without risk for humans. Like any other developed nation, Canada has used the UAVs for its military missions such as peacekeeping and for civilian missions such as coastal monitoring, surveillance of large areas and rescue operations. The applications of UAVs are very diverse, ranging from surveillance operations to combat operations through rescues. Advances in monitoring technologies, detection and computing have allowed these vehicles to perform missions without the direct control of the operator. The itinerary can be planned in advance and the UAVs can then run automatically. Planning efficient routes involves determining a specific solution with a precise objective like avoiding radar detection of the UAVs, finding the shortest path in terms of time, or minimizing the cost of the mission.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.041
GPT teacher head0.292
Teacher spread0.251 · 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