Designing and Implementing a 3DOF Robotic Arm for Color Sorting and Object Identification Using Computer Vision Technology
Bibliographic record
Abstract
This paper focuses on designing a three-degree-of-freedom robotic arm for color sorting using computer vision technology. It involves the use of a robotic arm composed of three joints and a gripper for grasping and manipulating objects. Each joint is actuated by a servo motor that is controlled by an Arduino microcontroller to control the angles of the joints and achieve the desired motion. The microcontroller relies on a camera to detect and analyze colors. The camera captures an image of the objects present in the designated area and converts it into digital data to be processed later by a computer. Then, the well-known (OpenCV) and (NumPy) library in the Python programming language is used to process this data and identify the colors. Once the color is determined, the corresponding Python command is executed through the Arduino microcontroller. That allows the robotic arm to move towards the designated location based on the identified color. Consequently, the arm grasps the detected object and places it in the appropriate position according to its color. The effectiveness of the proposed approach has been verified in real-time experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".