Real time autonomous collision avoidance for unmanned aerial vehicles
Bibliographic record
Abstract
GeoSurv II is a jointly funded project of Sander Geophysics Limited (SGL) and NSERC to develop a fixed-wing Unmanned Aerial Vehicle (UAV), capable of autonomously performing high resolution geophysical surveys at low flight altitudes over poorly known terrain. This thesis is in support of achieving this objective. In order to achieve such a level of autonomy, the UAV must be capable of avoiding stationary, pop-up and moving obstacles while flying at low altitude. Such obstacles may include power lines, communication towers, trees, unknown flight vehicles encountered while at flight or uneven terrains which creates the situation of the pop-up obstacles. In addition to that the UAV must be able to fly as close as possible to the reference trajectory for a given geophysical survey. The development and testing of a method capable of performing such an autonomous mission is the objective of this thesis. In this thesis, a method is developed based on a spectral method known as Legendre Pseudospectral Optimal Control, because of its capability to directly incorporate all of the mission objectives, while respecting the UAV constraints (which other methods in the literature are not capable of). The method accounts the aircraft and obstacle constraints there by capable of avoiding obstacles with feasible maneuvers for the aircraft. The objective to remain as close to the reference trajectory is fulfilled by setting the area between the flight trajectory and reference trajectory as the cost of optimization of the optimal control problem. Five different scenarios presented in this thesis show the developed method's capability to avoid the stationary, pop-up and the moving obstacles successfully while remaining close to the reference trajectory.
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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".