Adaptive Robotic Contour Following from Low Accuracy RGB-D Surface Profiling and Visual Servoing
Bibliographic record
Abstract
This paper introduces an adaptive contour following method for robot manipulators that originally combines low accuracy RGB-D sensing with eye-in-hand visual servoing. The main objective is to allow for the detection and following of freely shaped 3D object contours under visual guidance that is initially provided by a fixed Kinect sensor and refined by a single eye-in-hand camera. A path planning algorithm is developed that constrains the end effector to maintain close proximity to the surface of the object while following its contour. To achieve this goal, a RGB-D sensing is used to rapidly acquire information about the 3D location and profile of an object. However, because of the low resolution and noisy information provided by such sensors, accurate contour following is achieved with an extra eye-in-hand camera that is mounted on the robot's end-effector to locally refine the contour definition and to plan an accurate trajectory for the robot., Experiments carried out with a 7-DOF manipulator and the dual sensory stage are reported to validate the reliability of the proposed contour following method.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".