UAV Path Planning Using on-Board Ultrasound Transducer Arrays and Edge Support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it