Photogrammetry-based Dynamic Path Tracking of Industrial Robots Using Adaptive Neuro-PID Control Method and Robust Kalman Filter
Bibliographic record
Abstract
This paper proposes a novel accurate dynamic path tracking (DPT) method for industrial robots based on photogrammetry sensors and an adaptive neuro-PID (ANPID) control method. First, the pose of the robot's end-effector is detected by the photogrammetry sensor (C-Track stereo camera). It passes through a robust Kalman filter to reduce the noise in the signals. Then, the filtered signals are fed to the ANPID, whose gains are tuned online using an adaptive multi-layer perceptron neural network (AMLPNN). The steepest descent optimization method is adopted online. The cost function is the least mean square of the system states errors. Experimental results on FANUC M-20iA robot show the tracking accuracy reaches ±0.08mm and ±0.04deg, which exhibits the superiority of the proposed method over the conventional methods such as PID (tracking error±0.2mm and ±0.1deg) [4].
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".