A memetic algorithm approach for solving the task-based configuration optimization problem in serial modular and reconfigurable robots
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY This paper presents a novel configuration optimization method for multi degree-of-freedom modular reconfigurable robots (MRR) using a memetic algorithm (MA) that combines genetic algorithms (GAs) and a local search method. The proposed method generates multiple solutions to the inverse kinematics (IK) problem for any given spatial task and the MA chooses the most suitable configuration based on the search objectives. Since the dimension of each robotic link in this optimization is considered telescopic, the proposed method is able to find better solutions to the IK problem than GAs. The case study for a 3-DOF MRR shows that the MA finds solutions to the IK problem much faster than a GA with noticeably less reachability error. Additional case studies show that the proposed MA method can find multiple IK solutions in various scenarios and identify the fittest solution as a suboptimal configuration for the MRR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it