Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system
Bibliographic record
Abstract
Autonomous navigation of vehicles, especially drones, plays an essential role in Industrial Revolution 4.0. Maneuvering drone in complex path especially indoor environment requires stable and accurate navigation system. This paper investigates a navigation algorithm for maneuvering a drone by Sliding Mode Controller (SMC) combined by fuzzy logic system, model reference approach, and tag-based fiducial marker detection in an indoor environment. The SMC parameters are tuned by the fuzzy logic system and model reference approach. A drone model is simulated in a virtual indoor environment to validate the performance of the navigation system with different home points and trajectories. The desired set-points of the control system are obtained by AprilTag, which is a tag-based fiducial marker detection system. The stability of the SMC was verified using the Lyapunov stability theory. The performance of proposed SMC navigation algorithm validated by comparing to conventional controllers which represents the effectiveness of SMC. It can be ascertained that the proposed SMC navigation algorithm is applicable to maneuver the drone for various industrial tasks in indoor environment.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".