Fast path planning for unmanned aerial vehicle using embedded GPU System
Bibliographic record
Abstract
Unlike commercial airlines that fly predetermined trajectories, military unmanned aerial vehicles (UAVs) operate in dynamic environments and must often adjust their itinerary based on the developing conditions during the mission. The path planner module is a key element of any autonomous UAV. It computes the optimal path from a start point to an end point. In this paper, we present a parallel genetic algorithm for UAV path planning using an embedded NVIDIA Jetson TX1 single-board computer. The path is built as a series of line segment connected by circular arcs to remove discontinuities and to account for the dynamics of fixed wing UAVs. It is optimized to minimize the average altitude avoiding detection by enemy radars and to minimize fuel consumption improving range. The software developed is tested on four different 3D terrains. By exploiting the parallel architecture of the Jetson TX1 GPU, the proposed path planner provides a speedup of 33x compared to a sequential execution on an ARM processor. It calculates quasi-optimal solutions in complex 3D environments in less than 4 seconds and requires only 10 Watts, making it an excellent solution for onboard path planning.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".