Bibliographic record
Abstract
Recently, a robotic percussive riveting system has been developed at Ryerson University for an automation of percussive riveting process of aero-structural fastening assembly. The system consists of a robot holding a percussive riveting gun equipped with a rivet feeder, a gantry holding a working panel of aero-structure, and a position visual sensor. Prior to riveting, the robot is required to first position and then insert a rivet precisely into a hole on the panel without engaging the panel to prevent potential damage. The underlying challenges to precise insertion are various sources of system uncertainties, mainly including alignment errors among coordinate systems of the robot, panel and sensor, and relatively poor absolute positioning accuracy of the robot due to mechanical deflection, assembly clearance, and machining tolerance. For this reason, the research of relative pose estimation between the robot and panel has been carried out pertaining to these challenges. Essentially, pose estimation is proposed for robotic percussive riveting, which estimates the relative pose between two rigid bodies based on noisy visual measurements of point features on rigid bodies. Three categories can be classified, namely, static, dynamic, and robust pose estimation. Firstly, static pose estimation is the parameter estimation of static relative pose transformations among a number of frames, which solves the issue of alignment errors. Direct solutions of static relative pose estimation are derived based on least-square methods. Secondly, to tackle the issue of poor absolute positioning accuracy of the robot, dynamic relative pose estimation is proposed addressing a state estimation of relative poses during motion. Iterative extended Kalman filter method is adapted for the state estimation. Thirdly, for robustness against outliers of point measurements, robust pose estimation is proposed based on an outlier diagnosis using the technique of relaxation of rigid body constraints. Indeed, outlier diagnosis is a pre-processing of point measurements, in which outliers are detected and removed prior to the relative pose estimation. Further, a decorrelation method is proposed for measurement calibration using multivariate statistical analysis to find an optimal sensor-to-target configuration. As a result, each coordinate measurement is close to uncorrelated and it allows for a simple calibration.
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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 | 0.000 |
| 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".