Space Exploration - towards Bio-Inspired Climbing Robots
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".