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Record W2991543276 · doi:10.1115/detc2019-97163

A Novel Autonomous Scaled Electric Combat Vehicle

2019· article· en· W2991543276 on OpenAlexaff
Aaron Hao Tan, Haoxiang Lang, Moustafa El–Gindy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsChassisModular designObstacle avoidanceMobile robotObstacleSoftwareMotion planningComputer scienceRobotEngineeringEmbedded systemControl engineeringSystems engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Current literature pertaining to multi-steerable mobile platforms and the progression of military vehicles in the past few decades suggest a lack of effort in pursuing advanced technologies in this joint area. As a result, a novel 1:6 scaled electric combat vehicle prototype that features eight independently driven and steerable wheels is designed and developed in this paper. The intent is to create a scaled model for future autonomous vehicle navigation and control research in off road terrains. Starting with the mechanical design, this paper discusses the details of the chassis, suspension, driving and steering systems development. The electronics necessary for vehicle actuation is implemented with custom nodes and topics created for hardware communication within the Robot Operating System (ROS). Lastly, path planning and obstacle avoidance abilities are implemented to achieve autonomous navigation. The result of this work is a fully functional and instrumented robotic platform with a modular software architecture. Vehicle design analysis, performance and autonomous navigation abilities are experimentally tested with promising results.

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.

How this classification was reachedexpand

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 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: none
Teacher disagreement score0.943
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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