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Record W2562594515

Contribution à la commande de robots marcheurs bipèdes

2011· dissertation· fr· W2562594515 on OpenAlexaff
Ting Wang

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2011
Typedissertation
Languagefr
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsRobotiq (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophyPhysics
DOInot available

Abstract

fetched live from OpenAlex

The objective of this thesis is to study the walking control of biped robots including imbalance walking phase. Thus we dedicated to the stability analysis of periodical walking with under-actuation. Firstly, a planar biped robot with point feet is studied. Some tools for stability analysis such as Poincaré return map, Zero Dynamics and Hybrid Zero Dynamics are discussed. Two control laws based on joint reference motion (common in robotics) are proposed and compared with a control law for tracking parameterized reference trajectory (free temporal evolution). Next a biped robot with feet moving in 3D is discussed. In view of the importance of ZMP (Zero Moment Point) for the walking stability, it is regulated to its desired values. Moreover, a feedback control of the orientation of the swing foot is used to ensure a proper landing. As a result, some controlled outputs have been defined, so the robot can be dealt with like an under-actuated system. The approaches developed previously are then taken to obtain a stable walking. In addition, a steering control law is proposed to control the walking direction of the robot to pass through a door and reach a destination with a mild curvature. Finally, the proposed controller is extended to a biped robot with arms and a foot rotation phase that describing the stance heel lifts from the ground and the stance foot rotates about the toe is included in the single support phase. The proposed control can be naturally extended to this imbalance phase due to the control of ZMP, and the simulation results validate it is effective to obtain stable walking.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 designTheoretical or conceptual
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

Citations0
Published2011
Admission routes1
Has abstractyes

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