A Gaussian-Biased Heuristic for Stochastic Sampling-Based 2D Trajectory Planning Algorithms
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".