Research of Traversability for Tracked Robot on Slope with Unfixed Obstacles
Bibliographic record
Abstract
Investigation of active volcanoes by robots is required to grasp their situation. Considering that volcanic environments are rough terrain, tracked robots are suitable for the investigation. When a tracked robot travels on a volcanic environment, it must climb over obstacles. The obstacles on a volcanic environment can be roughly divided into “fixed obstacles” which can be moved by a robot and “unfixed obstacles” which cannot be moved by a robot. Although a tracked robot climbing over unfixed obstacles such as unstable rocks has risks of sliding-down and tipping-over, there is little research about climbing over unfixed obstacles. On the other hand, grousers on track belts are effective for climbing on fixed obstacles, such as steps or stairs. However, it is unclear whether the grousers are also effective for climbing over unfixed obstacles or not. Therefore, the research purpose is to reveal the effect of grousers for climbing over unfixed obstacles. In this study, the climbing experiment using a cylindrical obstacle and grousers with several conditions of height and gap was conducted. As a result, it was found that grousers also affect to improve the climbing performance for unfixed obstacles. Especially, higher grouser and grouser with a gap which is more than the size the obstacle just fits indicates better performance. Also, the sliding-down condition based on statics was derived to predict the climbing performance of tracked robots. Comparing the condition and the experimental results, it is reasonable for low-height grousers. According to the above research, it becomes clear that the effect of grousers on climbing performance for unfixed obstacles on a two-dimensional plane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".