Trajectory planning for surface following with a manipulator under RGB-D visual guidance
Bibliographic record
Abstract
This paper introduces a manipulator robot surface following algorithm using a 3D model of vehicle body panels acquired by a network of rapid but low resolution RGB-D sensors. The main objective of this work is to scan and dynamically explore regions of interest over an automotive vehicle body under visual guidance by closely following the surface curves and maintaining close proximity to the object. The work is motivated by applications in automated vehicles inspection and screening in security applications. The proposed path planning strategy is developed based on a perception-modeling-planning-action approach. Raw data rapidly captured by a calibrated network of Kinect sensors are processed to provide the required 3D surface shape of objects, normal measurements, orientation estimation, and obstacle detection. A robust motion planning method is designed that relies on this information, resulting in a safe trajectory that is planned to follow and explore the curved surfaces while avoiding collision with protruding components of the vehicle. The feasibility and effectiveness of the proposed method is validated through experimental results with a 7-DOF manipulator navigating over automotive body panels.
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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) | 0.001 | 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 | 0.001 |
| 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".