Localization of Electrical Outlet for a Mobile Robot Using Visual Servoing
Bibliographic record
Abstract
This paper presents the analysis, design, and the implementation of a system for detecting an electrical outlet that will allow a mobile robot to independently recharge its own energy supply. The system is implemented with a robotic arm, a zoom camera, a standard web camera and a laser distance sensor. Utilizing the eye-in-hand configuration the camera is mounted on end-effectors of the robotic arm. The image-based visual servo will control robot joint angles directly using measured image features. The image analysis is performed using Pattern algorithm. The robotic arm will start scanning the area trying to find an outlet. The system uses a template to be compared with the image coming from the camera; the zoom of the camera moves according to the distance of the object, which allows the system to compare with only one size template. Once the outlet has been identified, the coordinates x, y and z are sent to the robot. The robot will automatically move as close as necessary to the outlet then the robotic arm maneuvers using visual servoing until the plug face is perfectly aligned to the outlet; finally the plug/unplug system will be activated. Some simulation and experimental results will be given in the paper.
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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 | 0.001 |
| 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 | 0.001 |
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".