ON THE DYNAMIC TIP-OVER STABILITY OF WHEELED MOBILE MANIPULATORS
Bibliographic record
Abstract
Due to excessive maneuverability, mobile manipulators which consist of one or more manipulators mounted on a mobile base have attracted much of interest. Tipping over is one of the most important problems in mobile manipulators especially in manipulating heavy objects, also during maneuvers in unknown environment or on rugged terrains. Therefore, estimation and evaluation of dynamic stability with appropriate easy-computed measure throughout the motion of such systems is a challenging task. In this study, a new tip-over stability measure named as moment--height stability (MHS) measure is presented for wheeled mobile manipulators. The suggested MHS measure can be effectively used for both legged robots and mobile manipulators. The required computational effort of the MHS for a given system is compared to other measures, which reveals the efficiency of the MHS over the others. Finally, various case studies are presented to demonstrate the new MHS measure performance compared to the results of other measures. All calculations for system dynamics modeling have been performed using a symbolic code developed in Maple VI, and the obtained models were transferred to another code in Matlab VII to complete numerical simulations. The obtained results show the merits of the new proposed MHS measure, in terms of prediction of the exact time of instability occurrence, without extra unnecessary precautions which may confine the maneuverability of the system and its operation.
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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 | 0.000 |
| 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".