Real-world demonstration of sensor-based robotic automation in oil & gas facilities
Bibliographic record
Abstract
In this paper, a nonlinear signal-processing scheme is developed for robotic systems that exploits a joint state-parameter formulation for simultaneous recursive estimation of the states (e.g. joint angles and rates) and uncertain parameters (e.g. inertial and friction parameters), out of noisy measurements (e.g. joint angles). Unscented Kalman filtering was employed to overcome restrictions such as linearity in the parameters and the need for availability of joint velocities and accelerations (present in linear recursive least square methods), and the linearization problems associated with extended Kalman filtering. Owing to the unscented transform concept which requires only input-output evaluations of the dynamic model, a more general and modular implementation is realizable. This allows for the utilization of computational modeling tools without the requirement of symbolically manipulating or deriving the equations of motion. Also, the recursive nature of the scheme allows for both offline processing and online implementation. The practical performance of the proposed scheme was verified through an experiment involving a five-bar linkage based haptic device configured to render a virtual box. The torque pair commands generated by the haptic controller to render the virtual box and the encoder angular measurements acquired through the experiment were processed twice in two different input-output directions: once, for state-parameter estimation of the robot; and, another time for identification of supposedly unknown environmental parameters. Results demonstrate successfulness of the scheme for recursive state-parameter estimation of the robot and the environment, as well as promising applicability in online settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".