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Record W1546996719 · doi:10.5772/5505

Space Exploration - towards Bio-Inspired Climbing Robots

2007· book-chapter· en· W1546996719 on OpenAlexaff
Carlo Menon, Michael Murphy, Metin Sitti, Lydia Nicholas

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsClimbingSpace (punctuation)RobotComputer scienceArtificial intelligenceHuman–computer interactionGeographyArchaeology

Abstract

fetched live from OpenAlex

Robotic systems continue to be an important tool in space exploration, and as robotic systems continue to gain capability, the particular challenges of the space environment and the tasks required in space will benefit from such systems finding new roles in space. This chapter focuses on the design of climbing robots suitable for use in space. These mimic the climbing ability of geckos, using micro-structured hairs to provide dry adhesion, and exploit gait to exhibit rapid and robust motion over vertical surfaces on earth. Mobility of robotic systems allows many different roles for systems in space, including exploration on extraterrestrial surfaces and navigating vehicle surfaces in orbit. Climbing robots in space may provide extra capability in the ability to negotiate a broader range of terrains. The range of surfaces that might be required to be negotiated in space can be matched by the range of different possible climbing strategies. In particular, dry adhesive techniques inspired by the gecko are highlighted, with different robot designs intended to take advantage of such dry adhesives described in detail. Examples of other strategies for climbing robots are presented and discussed, particularly in the context of usefulness for future implementation in space. With this section introducing the subject and composition of this chapter, section 2 gives a brief overview of the main challenges facing robotic systems in space. Section 3 is devoted to the introduction of different strategies for climbing robots, including examples designed for use in space. Examples of robotic systems employing different approaches are given. In section 4, the subject of biologically inspired synthetic dry adhesion is introduced. Section 5 forms the major part of this chapter, describing the design and breadboarding of three climbing robots, intended to form the basis for future robots using such dry adhesives. Future work is discussed in section 6 and conclusions are given in section 7.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.055
GPT teacher head0.260
Teacher spread0.205 · 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 designBench or experimental
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

Citations12
Published2007
Admission routes1
Has abstractyes

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