Kinodynamic Motion Planning for Holonomic UAVs in Complex 3D Environments
Bibliographic record
Abstract
Due to their ability to hover, rotorcraft can be used in many cluttered and narrow environments that are unsuitable for other unmanned aerial vehicles (UAVs). However in such environments, it is usually dicult to nd paths that are both collision-free and dynamically feasible. This paper introduces a computationally ecient algorithm for nding safe paths, through known static three dimensional environments, that satisfy the kinodynamic constraints of a quadrotor. First, a collision-free path that ignores kinodynamic constraints is found using probabilistic roadmaps (PRM). Next, a heuristic re-sampling algorithm is used to make improvements to this path. Finally, the path is separated into a series of motion primitives that satisfy kinodynamic constraints. To ensure corner path segments remain collision-free, a method of bounding the vehicle deviation from the piece-wise linear path at each corner is introduced. Finally, the control inputs required to traverse the transformed path are calculated using kinematics and become the feed-forward inputs for a position controller. In simulation, the algorithm is able to successfully compute safe kinodynamic paths through cluttered and narrow environments, while using limited computational resources.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".