Design of Interactive Control System for Agricultural Machines Based on AR and Deep Reinforcement Learning
Bibliographic record
Abstract
Intellectualization of agricultural machinery can effectively improve the efficiency and quality of operations, and has an important role in promoting agricultural development.Based on AR technology, this paper introduces the key technology to build the interactive control system of agricultural machinery, uses NURBS to realize virtual agricultural machinery modeling, uses VRML technology to design a prototype of the scene environment of interactive farmland virtual reality, and details the methods of virtual modeling, virtual roaming, interactive control and collision detection in the process of system development.A four-degree-of-freedom simulation test bed is established to realize the simulation of the tractor's attitude when walking in the field.The position information of the crop rows is extracted from the virtual scene, and the control signals are given according to this information to carry out the speed, direction and balance control of the traveling of the agricultural machine, so that the tractor travels along the crop rows.The maximum deviations of the roll angle, pitch angle and yaw angle are within 0.36, and the maximum deviations of the elevation and traveling speed are 2.11 mm and 0.14 km/h.The simulation analysis and the physical test show the feasibility of the interactive control system of the farm machine.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".