Output-Feedback Image-Based Visual Servoing for Multirotor Unmanned Aerial Vehicle Line Following
Bibliographic record
Abstract
This article considers visual servoing-based motion control of multirotor unmanned aerial vehicles. We employ output feedback and image-based visual servoing to control the vehicle's pose with respect to a static planar visual target with a linear structure (e.g., electric transmission lines or pipelines). The method uses measurements from inexpensive sensors typically found on-board: an inertial measurement unit, and a monocular computer vision system. Unlike existing work, it does not require linear velocity, position measurements, or an optical flow sensor. The method directly controls the relative pose to the visual target and does not require global navigation satellite system measurements of the vehicle or target. The visual servoing method ensures the vehicle flies centered above the lines at specified height and yaw. Such motion control is important in a number of applications such as efficient data collection for infrastructure inspection. Our article exploits the inherent robustness of an image-based approach where feature error is computed directly in the image plane. A virtual camera is combined with output feedback and convergence of the closed loop is proven. The method is adaptive to vehicle mass, thrust constant, desired depth, and a constant disturbance force. Simulation and experimental results illustrate the method's performance and robustness to model uncertainty.
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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) | 0.000 | 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) | 0.001 | 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 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".