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Record W2801009918 · doi:10.1139/tcsme-2017-0020

Feedforward control for underactuated bipedal walking on varying compliant slopes

2018· article· en· W2801009918 on OpenAlexvenueno aff
Daojin Yao, Siyu He, Yao Wu, Xiaohui Xiao, Yang Wang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsUnderactuationControl theory (sociology)Feed forwardKinematicsBipedalismDamperController (irrigation)Computer scienceGaitStability (learning theory)RobotEngineeringControl engineeringControl (management)PhysicsArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

In this paper, a feedforward control strategy is proposed to enable stable underactuated bipedal walking on varying compliant slopes with a known inclination angle, to handle the variation in natural environments. First, spring–damper units were employed in the horizontal and vertical directions to model the compliant ground, which is described as a rigid kinematic chain coupled with a spring–damper system. Second, a new definition of stable underactuated bipedal walking, based on walking speed, was proposed. Stable walking is achieved by adjusting the velocity of the biped’s center of mass (CoM) within limits that have been proven to allow at least one walking cycle. The proposed feedforward control strategy was based on the motion state of a robot’s CoM, using the new definition of stability and inspired by the gait characteristics of human walking on varying slopes. Speed control is realized by adjusting the displacement of the CoM with the change of slope to achieve stable walking. Finally, simulations were conducted to validate the proposed controller. The simulation results demonstrate that stable walking is achieved on varying compliant slopes by implementing the proposed control strategy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicRobotic Locomotion and ControlFrench-language works237,207