MétaCan
Menu
Back to cohort
Record W2003305093 · doi:10.1109/robot.2010.5509162

A door opening method by modular re-configurable robot with joints working on passive and active modes

2010· article· en· W2003305093 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsModular designTorqueRobotHingeEngineeringWork (physics)Robot kinematicsComputer scienceControl (management)Mode (computer interface)Control engineeringParallel manipulatorMobile robotInternal forcesSimulationMechanical engineeringStructural engineeringArtificial intelligenceHuman–computer interaction

Abstract

fetched live from OpenAlex

In this paper, we study the problem of door-opening by using a modular re-configurable robot (MRR) mounted on a tracked mobile platform. The main concern of opening a door is how to prevent the internal forces that occur because of the positioning error or the imprecise modeling of the environment, i.e., the door parameters. Most previous research is based on compliant control, which makes the control system rather complicated. In addition, such approaches need expensive force/torque sensor to be implemented. With respect to the multiple working modes of the MRR modules, the complication has been avoided by switching the joints that has axis of rotation parallel to the door hinge to work in passive mode. As a result of this approach, the internal forces between the door and the mobile manipulator will vanish. Simulation results demonstrate the validity and efficiency of the proposed door opening 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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Quick stats

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicModular Robots and Swarm IntelligenceFrench-language works237,207