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Record W2154597835 · doi:10.1109/iros.2006.282167

Design of a Reconfigurable Space Robot with Lockable Telescopic Joints

2006· article· en· W2154597835 on OpenAlexaff
Farhad Aghili, Kourosh Parsa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsReconfigurabilityRobotActuatorModular designKinematicsMechanism (biology)Computer scienceControl engineeringRobot kinematicsMechanism designRoboticsEngineeringSimulationMobile robotArtificial intelligence

Abstract

fetched live from OpenAlex

This work presents a new paradigm and a conceptual design for reconfigurable robots. Unlike conventional reconfigurable robots, our design does not achieve reconfigurability by utilizing modular joints. Rather, the robot is equipped with passive joints, i.e., joints with no actuator or sensor, which permit changing the Denavit-Hartenberg parameters such as the link length and twist angle. The passive joints will become controllable when the robot forms a closed kinematic chain. Also, each passive joint is equipped with a built-in brake mechanism which is normally locked, but the lock can be released whenever the parameters are to be changed. Not only will such a manipulator have the versatility to perform different tasks but also it can be packed adequately within its designated space on the launch vehicle. Kinematics of such a robot is analyzed, and a stable control algorithm which can take the robot from one configuration to another is devised

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.185
Teacher spread0.171 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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