Design of Telerobotic Drilling Control System with Haptic Feedback
Bibliographic record
Abstract
The paper deals with the design of control algorithms for virtual reality based telerobotic system with haptic feedback that allows for the remote control of the vertical drilling operation. The human operator controls the vertical penetration velocity using a haptic device while simultaneously receiving the haptic feedback from the locally implemented virtual environment. The virtual environment is rendered as a virtual spring with stiffness updated based on the estimate of the stiffness of the rock currently being cut. Based on the existing mathematical models of drill string/drive systems and rock cutting/penetration process, a robust servo controller is designed which guarantees the tracking of the reference vertical penetration velocity of the drill bit. A scheme for on-line estimation of the rock intrinsic specific energy is implemented. Simulations of the proposed control and parameter estimation algorithms have been conducted; consequently, the overall telerobotic drilling system with a human operator controlling the process using PHANTOM Omni haptic device is tested experimentally, where the drilling process is simulated in real time in virtual environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".