The Automatic Drilling System of 6R-2P Mining Drill Jumbos
Bibliographic record
Abstract
In order to improve the efficiency of underground mining and tunneling operations and to realize automatic drilling, it is necessary to develop the automation system for large drill jumbos. This work focuses on one such mining drill jumbo which is actually a redundant robotic manipulator with eight degrees of freedom, because it has six revolute joints and two prismatic joints. To realize the autonomous drilling operation, algorithms are proposed to calculate the desired pose of the end-effector and to solve the inverse kinematics of the drill jumbo, which is one of the key issues for developing the automation system. After that, a control strategy is proposed to independently control the eight joint variables using PID feedback control approaches. The simulation model is developed in Simulink. As the closed-loop controllers corresponding to all joints are local and independent of each other, the whole system is not a closed-loop feedback control. In order to estimate the possible maximal pose error, the analysis of the pose error caused by the errors of the joint variables is conducted. The results are satisfactory for mining applications and the developed automation system is being applied in the drill jumbos built by Mining Technologies International Inc.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".