Climbot: A Bio-Inspired Modular Biped Climbing Robot—System Development, Climbing Gaits, and Experiments
Bibliographic record
Abstract
Agriculture, forestry, and building industry would be prospective fields of robotic applications. High-rise tasks in these fields require robots with climbing skills. Motivated by these potential applications and inspired by animal climbing motion, we have developed a biped climbing robot—Climbot. Built with a modular approach, the robot consists of five joint modules connected in series and two special grippers mounted at the ends, with the scalability of changing degrees-of-freedom (DoFs). With this configuration, Climbot not only has superior mobility on multiple climbing media, such as poles and trusses, but also has the function of grasping and manipulating objects. It is a kind of “mobile” manipulator and represents an advancement in development of climbing robots. In this paper, we first present the development of this climbing robot with modular and bio-inspired methods, and then propose and compare three climbing gaits based on the unique configuration and features of the robot. A series of challenging and comprehensive experiments with the robot climbing in a truss and performing an outdoor manipulation task are carried out, to illustrate the feasibility, the features, the climbing, and manipulating functions of the robot, and to verify the effectiveness of the proposed gaits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".