Air Shepherd: Trajectory Prediction-Based Target Localization and Circumnavigation in Cluttered Environments
Bibliographic record
Abstract
This paper proposes a trajectory prediction-based target localization and circumnavigation pattern for cluttered three-dimensional environments, which is more realistic and suitable for more complex environments than traditional patterns. The main work of the paper consists of two parts: tracking based on trajectory prediction and circumnavigation based on broadcast information. On the one hand, the tracking Autonomous Aerial vehicle (AAV) obtains target trajectory prediction based on the B-spline curve, and then achieves target localization and tracking through front-end search and back-end optimization. On the other hand, without communicating with each other, a distributed control strategy is presented so that the multiple circumnavigation AAVs can achieve target circumnavigation and reciprocal avoidance by only observing the status of adjacent AAVs. In the simulation, obstacle avoidance vehicles moving freely at different speeds are selected as targets in two scenarios and the simulation results are given to verify the effectiveness of the proposed approach. Furthermore, a hardware-in-the-loop experiment and a overall system validation experiment are designed to verify the feasibility of the algorithm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".