UAV collision avoidance using cooperative predictive control
Bibliographic record
Abstract
This article describes the use of predictive control for the decentralized cooperative control of unmanned aerial vehicles in an unfamiliar three-dimensional environment. It is assumed that each vehicle is equipped with an autopilot and a trajectory control unit. The autopilot insures the stability of the vehicle. The setpoints of the autopilot are calculated by the trajectory control unit, thus forming a cascade control structure. The trajectory control unit relies on a predictive control algorithm to calculate the optimal commands (autopilot setpoints) such that the vehicle will reach fixed targets at known positions while avoiding static obstacles that are detected en route. The advantage in using predictive control is that it offers great flexibility in the objective function to optimize while respecting constraints such as command limits, limits on the displacement that a vehicle can carry out, and the constraints that allow obstacle avoidance. The principle proposed in order to avoid static obstacles (that are assumed ellipsoid) is to verify that the predicted vehicle trajectory does not intersect these obstacles. With the objective to increase performance, cooperation between vehicles must also be privileged. Thus, if some vehicles are within the communication range, they can share the position and shape of the obstacles they have detected. Simulations illustrate the method and higlight the benefits of cooperation.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".