Track-stair and vehicle-manipulator interaction analysis for tracked mobile manipulators climbing stairs
Bibliographic record
Abstract
This paper analyzes interactions between the tracks and the stairs, as well as those between the tracked mobile robot and the onboard manipulator for tracked mobile manipulators (TMMs) climbing stairs. Combining a tracked mobile robot, which has the ability to climb stairs, with an onboard manipulator, a TMM extends the workspace and scope of applications of the robot dramatically. However, this combination gives rise to complex track-stair and vehicle-manipulator interactions, because the configuration of the onboard manipulator affects load distribution, which will further influence the track-stair interactive forces. Unlike the wheeled mobile robots, which are normally assumed to obey the nonholonomic constraints, slippage is unavoidable for a tracked mobile robot, especially when climbing stairs. The track-stair interactive forces are complicated, which may take the forms of grouser-tread hooking force, track-stair edge frictional force, grouser-riser clutching force, and even their compositions. In this paper, the track-stair and vehicle-manipulator interactions are analyzed systematically, which are essential for tip-over prediction and prevention, as well as for automatic control of TMMs in autonomous and semi-autonomous stair-climbing. Simulations for a TMM being developed in our laboratory have demonstrated the usefulness of the presented analysis results.
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 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 | 0.001 | 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".