Development of a Physical Analog Excavator for Studies in Interactions Between Hydraulic Equipment and Human Operators
Bibliographic record
Abstract
Abstract This paper presents a scale model excavator intended to allow for studies on human-machine interactions. In the past, this work has been performed on full-scale equipment which can be dangerous and costly to acquire and operate, or fully in simulation, which requires high precision models of complex effects such as soil forces (e.g. using the Discrete Element Method, DEM, simulating thousands of particles). Also simulation models with scenes projected on computer monitors or in virtual reality may not be realistic enough for the human operator to be fully immersed and behave in a realistic manner. The motion of the small-scale excavator presented here is directed by a digital model of a hydraulic machine given inputs of operator commands and actuator forces, allowing it to mimic the behavior of various hydraulic architectures (e.g. pressure compensated load sensing vs open center vs closed center systems). The excavator is used to dig in physical soils, generating realistic soil-tool interaction forces (which are fed back into the digital machine model) without the need for computationally expensive DEM models. This allows for rapid, low-cost evaluation of various hardware modifications as well as human-machine interaction effects. We also present some preliminary data from a pilot study investigating energy efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".