Optimization-based design of a novel hybrid aerial/ground mobile manipulator
Bibliographic record
Abstract
This paper is concerned with the mechanical design of a mobile manipulating unmanned ground vehicle (MM-UGV) coupled with an existing commercial unmanned aerial vehicle (UAV) to create a novel hybrid aerial/ground mobile manipulator. A novel systematic optimization-based approach is presented for making important design choices, such as the selection of gearboxes and electric DC motors in the drive-train, manipulator link lengths, and UGV base length. The objective is to minimize the overall mass of the MM-UGV. Constraints related to workspace, dynamic tip-over stability, actuator torque/force limits in static and dynamic motions, executed in the air or on the ground, and battery properties are incorporated into the problem formulation. The system operating conditions in the form of the range of end-effector forces, operating surface grade, and various position, velocity and acceleration variables are provided by the designer. The resulting problem is a robust bilevel nonlinear optimization, in which some of the constraints are derived from maximization/minimization over the operational variables to ensure constraint satisfaction in all possible operation scenarios. The problem is solved using a genetic algorithm. Numerical simulations demonstrate that the proposed strategy produces a design that meets the user-specified requirements.
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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) | 0.002 | 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".