Module-Based Static Structural Design of a Modular Reconfigurable Robot
Bibliographic record
Abstract
In this paper, the structural design of modular reconfigurable robots (MRRs) is studied. This problem is defined as the determination of proper module sizes according to the robot’s payload and end-effector deflection specifications. Because an MRR has multiple configurations, a simple design process is proposed in order to avoid performing the structural design stage at each configuration. The final structural design is only carried out at a single configuration that can guarantee the robot’s satisfactory performance for all remaining feasible configurations. It is shown that the module structural design stage can be performed at the local coordinate frame of each module. While the module local force requirement can be fully determined, the determination of the module local deformation requirement is redundant. Thus, there can exist multiple design solutions. To overcome this problem, a nonlinear approach using a genetic algorithm is used to search for an optimal solution. Finally, a design simulation is performed on an example MRR, and the results show the effectiveness of the proposed design method.
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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 | 0.001 | 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".