Optimization of a Mobile Platform for a Wheeled Manipulator
Bibliographic record
Abstract
A method for optimizing a mobile platform to form a wheeled manipulator is presented. For a given manipulator, this mobile platform is optimized to have maximum tip-over stability against the reaction forces and moments caused by the movement of the manipulator. This optimization is formulated as a max–min problem, i.e., to maximize a stable region ratio (SRR) over the manipulator's workspace while minimizing a tip-over moment (TOM). For a practical solution, this max–min problem is converted to two subproblems. The first one is the worst-case analysis to determine the maximum positive value of TOM through searching over the manipulator's workspace. A positive value of TOM indicates tip-over instability. The three parameters used for this search are pertaining to the mobile platform itself, i.e., the number of support wheels, the size, and mass of the mobile platform. The second subproblem is to optimize the placement of the manipulator and accessory on the mobile platform against the identified worst case so that the entire manipulator's workspace is stable. The effectiveness of the proposed method is demonstrated by applying it to optimize a mobile drilling and riveting robot.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".