Optimal control-based quantum genetic algorithm for a six jointed articulated robotic arm
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores the use of a quantum genetic algorithm (QGA) for finding the best control considering a calculated path for a six-jointed robotic arm. Classical genetic algorithms (GAs) are typically used to solve optimization problems in robot manipulators, however, QGAs bring a consistent advantage in terms of solution quality. In fact, this study uses a QGA simulated on classical hardware to create optimal control law based on a fifth-order polynomial path, aiming to minimize the tracking error of the position. Eventually, it compares positional error and energy consumption used by actuators through its cost function with to the classical methods. The simulation demonstrates that the QGA tends to be better than real-coded and binary-coded genetic algorithms (respectively RCGA and BCGA), especially when it comes to tracking accuracy, energy, and maintaining stability in noisy conditions. This indicates its potential use in real-time robotics applications by exploring quantum algorithms' practical benefits over traditional optimization methods for complex tasks with multiple dimensions in robot systems control.
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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