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Record W4255926899 · doi:10.24018/ejeng.2020.5.12.2256

Multi-terrain Quadrupedal-wheeled Robot Mechanism: Design, Modeling, and Analysis

2020· article· en· W4255926899 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal of Engineering and Technology Research · 2020
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsRobotControl engineeringFinite element methodMechanism (biology)EngineeringControl theory (sociology)Mechanism designServomotorTerrainComputer scienceSimulationMechanical engineeringArtificial intelligenceStructural engineeringMathematics

Abstract

fetched live from OpenAlex

For a robot to navigate in terrains of rough and uneven topographies, its drives and controllers must generate and control large mechanical power with great precision. This paper is aimed at developing an autonomous robot with active-suspensions in form of a hybrid quadrupedal-wheel drive mechanism. This involves a computational approach to optimizing the development cost without compromising the system’s performance. Using the Solidworks CAD tool, auxiliary components were designed and integrated with the bed structure to form an actively suspended robot drive mechanism. Also, using the S-Math Computing tool, the robot’s suspension system was optimized, employing a four-bar mechanism. To enhance the compatibility of this design with the intended controller, some mathematical equations and numerical validations were formulated and solved. These included the modeling of tip-over stability and skid steering, the trendline equations for computing the angular positions of the suspension servomotors, and the computation of R2– values for determining the accuracy of these trendline equations. Using finite element analysis (FEA), we simulated the structural integrity of key sub-components of the final structure. The results show that our mechanical design is appropriate for developing an actively suspended robot that can efficiently navigate in different terrestrial sites and topographies.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.277
Teacher spread0.219 · 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