Optimal Sampling-Based Trajectory Planning For Autonomous Systems in Urban Environments
Bibliographic record
Abstract
Motivated by autonomous aerial vehicles, this thesis provides a methodology for optimal trajectory planning of affine systems in non-convex environments. The resulting approximation of the optimal trajectory can then be provided to a flight controller as a reference trajectory, which compares the actual state of the system with the reference trajectory and performs the necessary control input corrections. More specifically, a modified trajectory planner inspired by Kinodynamic RRT* is presented to solve optimal control problems for input constrained affine systems with non-convex state spaces. As a result, if a solution is obtained then the solution is guaranteed to verify the state and control input constraints of the problem. Additionally, a randomized sampler function is proposed for Kinodynamic RRT* using a Gaussian distribution across the system’s state space. When the distribution is adequately sized lower cost approximate solutions of the optimal trajectory problem is obtained in less computation time when compared with other methods in the literature. The results are successfully applied to optimal control problems for an affine double integrator with drift that is subject to a maximum control input magnitude in non-convex environments.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".