Kinodynamic Motion Planning for UAVs: A Minimum Energy Approach
Why this work is in the frame
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Bibliographic record
Abstract
We present an optimal kinodynamic rapidly exploring random tree, a single query incremental sampling based optimal motion planner for robots with non-linear dynamics, differential constraints and actuation limitations. Our work extends the algorithms presented previously by formulating a fixed-final-state-free-final-time open loop configuration space metric for nearest neighbours search and the appropriate closed loop feedback controller for the tree extension heuristic, allowing us to introduce constraints on actuation magnitude and bandwidth. The controller is formulated by minimizing the amount of energy used to connect two states whereas the trade off is between total trajectory time and weighted actuation norm. We demonstrate the algorithm on (1) a simple 2D pendulum with actuation constraints and (2) a quad rotor 13D model.
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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 | 0.000 |
| 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 | 0.001 |
| 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