Vision-Based Autonomous Landing and Charging System for a Hexacopter Drone
Bibliographic record
Abstract
One of the challenges in Drone-based systems is the limited capacity of the onboard battery.To overcome the limitation of the onboard battery capacity, an intelligent decisionmaking system for autonomous landing and charging processes is presented in this paper.The system aims to recharge drained battery and extend flight duration.It based on the infrared light-emitting diodes (LEDs) detection and marker recognition.A novel landing pad with twenty infrared LEDs and eight barcodes is carefully designed and used in this research.The landing process is divided into two phases.During the first phase, the LEDs are observed by a camera that is equipped with an infrared-pass filter, while the barcodes are observed by two Pixy cameras in the second phase.To land the Drone on the proper polarity and then start recharging process, a hierarchical vision-based autonomous landing algorithm (HVALA) based on Otsu thresholding method and Laplacian of Gaussian (LOG) operator is proposed.The whole system has been designed and tested through a series of autonomous flights.The experimental results, obtained during the final phase of the landing process confirm the feasibility and robustness of the system where a small error of 4.4cm on average was observed with maximum landing time of 10 seconds.Such error is acceptable in this application and leads to a higher landing success rate.
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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 | 0.001 | 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".