Vision-based adaptive prediction, planning, and execution of permissible and smooth trajectories for a 2DOF model helicopter
Bibliographic record
Abstract
Vision-based control of Unmanned Aerial Vehicles (UAV) is gaining a global interest. Recent quantum leaps in the development of fast image acquisition–processing tools are making vision sensors omnipresent. Information obtained from the on-board imaging sensor of a UAV can be used for mapping the environment, localizing the UAV, visual odometry, and tracking pre-specified trajectories and (or) way points. The information obtained through vision sensors can be either fused with those obtained from a GPS or can be used in GPS-deprived scenarios such as indoor applications. A vision-based control strategy based on an adaptive prediction, planning, and execution framework is proposed with the objective to smoothly servo–track an object in near optimal time. A class of C2 continuous quintic polynomial based trajectories is planned at a higher level first taking the maximum permissible acceleration of the flyer into account. At a lower level, a Linear Quadratic regulator is used to track the planned trajectory. The replanning is carried out under two conditions: (i) when the flyer fails in tracking the planned trajectory closely, or (ii) the target object to track starts moving. This framework was tested on a 2 degrees of freedom model helicopter equipped with an on-board pinhole perspective camera via simulations.
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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 | 0.000 | 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 | 0.000 |
| 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 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".