Simulation Study of a Control Procedure for Automated Loading of Bulk Media
Bibliographic record
Abstract
Automation of loading of bulk media, or autoloading by an excavator, implies that an excavator for soft rock or particulate material loads its bucket successfully without the intervention of an operator. The subject has been under investigation for several years now. Nevertheless, the result of research has not yet reached to the level of implementation and commercialization because of two main reasons. The first and major reason is that the process of loading in itself is complicated and difficult to control. This is common to all the excavating machines and the results, when available, can be adapted by any specific machine. The problem to be resolved is "What has to be controlled and how". The second reason is the fact that autoloading is a sub-task of autonomous excavation where many other tasks are involved. Before all of these tasks can be automatically performed perfectly, a successful operation cannot be expected. The fact that not all of the excavating machines function in the same way has made the latter more complicated, since not all the previous pertinent work has been focused on automation of one particular type of excavator. This paper concerns the first category problem and is targeted for finding the solution to the automation of the loading process only. The previous work has led to the appropriate approach (proposal) for the control of the process: At a higher level control the trajectory of the loading/digging/cutting bucket is determined(and adjusted) based on the measurement of the interaction forces; at a lower level, the motion of the bucket is controlled based on the required motion (position and velocity). Before having access to a real system, we have decided to study the results by simulation. This work reports the latest results of implementing this control strategy by simulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".