Robust Photogrammetry Sensor-Based Real-Time Pose Control of Industrial Robots
Bibliographic record
Abstract
This article proposes a novel robust approach for accurate real-time pose control (RPC) of industrial robots based on photogrammetry sensors. The proposed method comprises two main parts: accurate pose detection using a photogrammetry sensor and robust Kalman filter (RKF) and RPC of the robot's end-effector utilizing a chattering-reduced sliding mode controller (CRSMC) with a nonlinear sliding surface. An eye-to-hand photogrammetry sensor (C-Track ATEMEK) detects the robot's pose, and then RKF filters out the noises in the detected pose signals. These filtered signals are fed to the CRSMC, which exploits a fast-nonlinear reaching law and nonlinear sliding surface to provide robustness against existing uncertainties, high tracking accuracy, and fast convergence speed. The stability of the CRSMC in the discrete-time domain is proved, and its performance is analyzed. The simulation results demonstrate the superiority of the proposed approach to other methods in terms of convergence speed, control effort, chattering level, and tracking accuracy. Experimental results on a PUMA 200 robot also show an unprecedented tracking accuracy, i.e., ±0.07 mm and ±0.17° for position and orientation, respectively.
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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) | 0.001 | 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".