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Record W4322213382 · doi:10.17559/tv-20221206142059

Kinematics Analysis and Simulation of Biped Transportation Robot in Stair Climbing Scene

2023· article· en· W4322213382 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

VenueTehnicki vjesnik - Technical Gazette · 2023
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsMD Precision (Canada)
FundersNatural Science Foundation of Guangdong Province
KeywordsKinematicsStair climbingClimbingComputer scienceRobotSimulationEngineeringPhysical medicine and rehabilitationArtificial intelligencePhysicsStructural engineeringMedicineClassical mechanics

Abstract

fetched live from OpenAlex

Difficult and labour-intensive transportation up and down the steps is a challenge in small spaces. This work focused on the smooth performance requirements about each joint structure of robot foot. We have made research on the hand and foot structure of the transport part, which simplifies its posture calculation in the designed bipedal transport robot model. Methods for determination of each joint angle of the hand and foot are presented, including the use of standard D-H method to establish a mathematical model and perform forward and inverse kinematic analysis to derive the angles of each joint, where the matrix was coded by Python software to ensure the accuracy of the calculated results. The joints angles are assigned by the Step function, and the motion of the bipedal robot in the case of single access is simulated by the inverse kinematic derivation formula. Simulation results show that the structure of the robot's joints meets the requirement of maintaining the stability of the robot when going up and down the stairs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.023
GPT teacher head0.293
Teacher spread0.271 · 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