An Artificial Neural Network Approach to Payload Estimation in Four Wheel Drive Loaders
Bibliographic record
Abstract
Estimation of the manipulated payload mass in off-highway machines is made non-trivial by the nonlinearities associated with the hydraulic systems used to actuate the linkage of the machine in addition to the nonlinearity of the kinematics of the linkage itself. Hydraulic cylinder friction, hydraulic conduit compressibility, linkage machining variation and linkage joint friction all make this a complex task under even ideal (machine static) conditions. This problem is made even more difficult when the linkage is mobile as is often the case with off-highway equipment such as four-wheel-drive loaders, cranes, and excavators. The rigid body motion of this type of equipment affects the gravitational loads seen in the linkage and impacts the payload estimate. The commercially available state-of-the-art load estimation solutions rely on the mobile machine becoming pseudo-static in order to maintain accuracy. This requirement increases the time required to move the material and decreases the productivity of the machine. An artificial neural network solution to this problem that enables the machine to remain dynamic and still accurately estimate the payload is discussed in this paper. Development and implementation on an actual four-wheel-drive loader is shown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".