Novel design and motion analysis of an omni-tread snake-like robot for narrow space inspection
Bibliographic record
Abstract
Snake-like robots have a slender body and strong environmental adaptability. This paper aims to propose an omni-tread snake-like robot that can adapt to the needs of specific narrow space exploration missions and provide a basis for design work through necessary motion analysis. The robot adopts the form of three modules in series, and the modules are connected through differential driving joints, which increases the joint torque of the robot. The nested omni-tread structure improves the robot's motion efficiency and environmental adaptability. This paper conducts configuration and motion analysis of the robot. The simulation is performed using the optimization solution method to obtain the joint torque and walking torque of the robot, which guides the design of the robot. The experiment is also conducted with the developed prototype to verify the robot's performance. From the simulation results, the joint torque and walking torque are obtained, and the motors are selected. The motion performance and field applicability of the robot are verified through experiment tests. The experiment results further verify the robot design and analysis work. The structural design of robots and optimization solution method in this paper has certain reference values for other researchers.
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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".