Integrated Navigation by a Greenhouse Robot Based on an Odometer/Lidar
Bibliographic record
Abstract
During greenhouse operations, robots need a specific working path to perform highprecision cruises, and thus, we designed a navigation positioning system based on an odometer/lidar.The navigation positioning system consists of a supervision terminal and a mobile robot.The supervision terminal releases map composition and cruise tasks, and the mobile robot composes a two-dimensional environment map, plans the cruise path and engages in navigation positioning; together, the two perform remote data exchanges through a wireless network.The robot could collect encoder data and obtain mileage information through track deduction, and combined with lidar data and the Gmapping algorithm, a two-dimensional environmental map was established.This system employs an A* algorithm to plan the cruise path and uses AMCL to estimate the position and pose of the robot.Based on the application of an expandable A* algorithm in the navigation toolkit of ROS, a specific working path cruise could be performed by setting goal points.The test results show that the navigation positioning system could perform a specific working path cruise; the average deviation in its straight line walk is 3.34 cm.The average deviation in the specific working path cruise is 2.73 cm, and the system has relatively higher navigation positioning precision because it could finish the specific path cruise and could better satisfy the greenhouse navigation positioning requirements than previous options.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".