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Record W648617424

PATH TRACKING, OBSTACLE AVOIDANCE AND DEAD RECKONING BY AN AUTONOMOUS PLANETARY ROVER

2014· article· en· W648617424 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

VenueInternational Journal of Heavy Vehicle Systems · 2014
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsCarleton University
Fundersnot available
KeywordsDead reckoningObstacle avoidanceComputer visionOdometryTrajectoryInertial measurement unitSensor fusionArticulated vehicleObstacleComputer scienceArtificial intelligenceCollision avoidancePath (computing)EngineeringMobile robotGlobal Positioning SystemAerospace engineeringRobotGeographyCollision
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a set of algorithms for piloting an autonomous planetary rover along a planned path, performing real–time obstacle avoidance and improving a dead reckoning capability. Path tracking is accomplished using linear regulation (feedback) of position and orientation errors, measured with respect to the planned path trajectory. Obstacle avoidance is performed through the application of the concept of an artificial potential field to data that can be acquired using a scanning rangefinder. Dead reckoning is improved by the algorithmic filtering and fusing of odometry and inertial navigation data streams. Computer simulation is used to illustrate the path–tracking and obstacle–avoidance capabilities, and experimental data is used to show how sensor fusion mitigates the effects of wheel–slippage and integration–error in dead reckoning.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.575

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 communication0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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