Motion Control of a Cable Robotic LED Light Fixture with IoT Connectivity<sup>**</sup>
Bibliographic record
Abstract
This paper presents development of a 2-degrees-of-freedom cable-suspended motion mechanism for a light-emitting diode (LED) horticultural light fixture with Internet-of-Things (IoT) connectivity. The proposed light motion mech-anism is intended to provide uniform light distribution on the plant canopy to optimize growth through proper position and orientation control with set-points determined, for instance, by natural light conditions and based on the plant's growth stage. The setup was built to have two main features: (i) Motion control of fixture's height and roll angle; and (ii) IoT connectivity. The motion control unit consists of a trajectory planner, which receives the control set-points via an IoT module, and embedded firmware. The reference values are obtained, for instance, through a light recipe database that provide the required data from a remote digital twin computer. The desired motion trajectory is obtained based on the dynamics of the system such that the tension of cables remain positive during the motion. To this end, conditions are obtained for the desired motion trajectory to guarantee positive tension in the cables which is utilized in the trajectory tracking controller. A hardware prototype was built to evaluate performance of the system which is presented along with experimental results.
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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".