The development of a geo-referencing system for machine controlled construction equipment
Bibliographic record
Abstract
Machine controlled construction equipment (MCE) continues to garner support from the construction industry due to shortages of skilled labor, constant technological advances and the importance of construction to the overall economy. MCE requires reliable, real-time geo-referencing of the equipment end-effector. However, MCE research continues to focus on relative positioning for control system design, while research dealing with geo-referencing has been mainly in the fields of aerial mapping and terrestrial mobile mapping, which can benefit from post-processing. The research described in this thesis attempts to overcome this problem by developing a real-time geo-referencing system specifically for MCE. The total system consists of three components; an integrated DGPS/INS to geo-reference the equipment main-body; an open kinematic chain to relatively position the end-effector with respect to the main-body; and a unified model to geo-reference the end-effector. The system carrier is designed for an excavator, but the model for the development of the open kinematic chain, designed using the Denavit-Hartenberg convention, can accommodate any type of MCE. The overall objective was the development of a precise geo-referencing system that could be operated under all construction conditions and could achieve an accuracy required for the recording of exposed infrastructure which calls for a vertical component of 15mm. This required high-level accuracy in both the position and orientation, therefore, DGPS and INS were integrated. Furthermore, the positional accuracy required double-differenced carrier phase measurements implemented using a least squares method for ambiguity resolution. Extended Kalman filters (EKF) were used for DGPS baseline estimation and DGPS/INS integration, the latter using a tightly-coupled, closed-loop architecture. Finally, error analysis was completed on the open kinematic chain to resolve the accuracy required in the joint sensors. System testing was completed using sample data from an International Federation of Surveyors Commission mobile van test and simulated data for the open kinematic chain. Results showed that the geo-referencing system was able to achieve ±0.024m (RMSE) horizontally and ±0.034m (RMSE) in height when the excavator was stationary and executing a common digging trajectory. The accuracy achieved would allow the excavator to operate autonomously for several common construction tasks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".