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Record W2108789672 · doi:10.1109/cdc.1995.480223

Jumping height control of an electrically actuated, one-legged hopping robot: modelling and simulation

2002· article· en· W2108789672 on OpenAlexaff
Mehran Mehrandezh, Brian Surgenor, S.R.H. Dean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsQueen's University
Fundersnot available
KeywordsJumpingControl theory (sociology)JumpRobotTrajectorySettling timeSensitivity (control systems)SimulationLegged robotComputer scienceStability (learning theory)EngineeringControl (management)Control engineeringPhysicsArtificial intelligenceStep response

Abstract

fetched live from OpenAlex

The problem of one dimensional hopping robots has been extensively studied over the years. This paper revisits the problem from the standpoint of evaluation of a novel form of actuation. A one legged hopper is modelled and simulated, then analyzed primarily on the basis of dynamic stability. A novel mechanical design of the electrically actuated robot leg is introduced that is simpler than those previously reported in the literature. A new method is proposed to evaluate the number of hops required to attain a desired jumping height. Three methods are examined to control the jumping height. The design of the final control law is an improvement over previous efforts. This law is introduced using the robot's trajectory for a full jump cycle (starting at the top of a jump), then enhanced in order to achieve near zero steady state error and a shorter settling time. The final control law shows minimal sensitivity to system parameters such as spring type or surface characteristics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.201
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations13
Published2002
Admission routes1
Has abstractyes

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