Agile unmanned vehicle navigation in highly confined environments
Bibliographic record
Abstract
Current Unmanned Vehicle (UV) navigation systems are capable of autonomous navigation among disperse obstacles. However, these systems may fail to guide vehicles through highly confined environments because they do not explicitly consider the geometry of the vehicle in the navigation task. This paper presents a methodology that enables the navigation of Unmanned Vehicles (UVs) in such 3D environments. The proposed approach uses a hybrid navigation architecture which employs a global path planner and a local obstacle avoidance methodology in parallel and combines them utilizing an improved Model Predictive Control (MPC) approach that incorporates the geometry of the UV in the cost function. Using MPC enables the UV to generate complex maneuvering trajectories while avoiding obstacles, respecting the dynamic characteristics of the UV and preventing state and input saturation. Simulations in 2D and 3D demonstrate the effectiveness of the proposed method for the navigation of a highly maneuverable Rotary Unmanned Aerial Vehicle (RUAV) in a highly confined environment.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".