UAV Path Planning Using on-Board Ultrasound Transducer Arrays and Edge Support
Bibliographic record
Abstract
As UAVs become increasingly autonomous with decreased physical size, it will become more difficult for human operators to perform a demanding job with many challenges. These challenges include keeping track of UAVs in low-level airspace while considering the psychological state of the operators. The main objective of the operator is to eliminate the high risk of UAV collisions with each other or with unexpected obstacles while in flight. By using AI, the operators psychological state can be assessed in a non-intrusive manner, while equipping the system with the capability to take over and activate the teleoperated mode. In particular, affective computing sensing can aid the user in the teleoperated mode when combined with a particular control technique. A control technique is proposed on the basis of sensory measurements for adjusting the velocity and direction, therefore averting obstacle collisions. Ultrasound sensors are used to provide more real-time local data, however, these sensors can be vulnerable to missing data. To mitigate the issue of sensors with missing data, GANs can generate synthetic values that can be used in an AI-based prediction algorithm to direct the UAV on a path. In this paper, real-time data is collected through on-board Ultrasound sensors mounted on a commercial UAV. The collected data is reported to the edge server where the GAN is implemented along with the ML algorithm to predict the path. The results demonstrate the effectiveness of this approach with an accuracy of more than 96.96%.
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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 | 0.000 | 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".