A Gaussian-Biased Heuristic for Stochastic Sampling-Based 2D Trajectory Planning Algorithms
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Bibliographic record
Abstract
This paper addresses the problem of slow convergence for stochastic sampling-based trajectory planners with applications to Unmanned Aerial Vehicles. Typically, stochastic sampling-based trajectory planners apply a uniform probability distribution to the vehicle's configuration space for random sampling. This results in execution times that make these planners not applicable to many real-time systems. The proposed method obtains first the obstacle-free trajectory according to the trajectory planner's steering function. A minimum area bounding ellipse is then defined for the obstacle-free trajectory and is expanded to satisfy a given maximum obstacle intersection area. The resulting elliptical surface is then converted to a Gaussian distribution for randomly generating a given percentage of samples in the interior or along the boundary of the elliptical surface. The proposed Gaussian-biased sampling strategy is applied to a minimum time trajectory planning problem and is compared with a uniformly distributed sampling strategy as well as two other sampling strategies taken from the literature. Simulations results show that the proposed sampling strategy yields a reduction of the computation time for producing an initial trajectory, of the initial trajectory cost, of the final trajectory cost, and of the algorithm's failure rate. Additionally, the proposed Gaussian-biased sampling strategy naturally inherits the completeness and optimality properties of the trajectory planning algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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