Application of Feedforward Control to Pan-Tilt Cameras on Planetary Rovers
Bibliographic record
Abstract
Future rover missions will be enhanced through the addition of science to the planetary traverse phase. Scientific targets are selected through a random search and salient gradient tracking in the visual field, which requires both a search algorithm and a reactive pan-tilt camera controller. This thesis presents a cerebellar-like reactive pan-tilt controller to track salient targets in the visual field as the rover moves based off the cerebellar models and the human vestibulo-ocular reflex. An online neural network using an EKF training law is used as a feed forward controller and it's performance is compared to standard batch and online neural network training techniques. The controller was then applied to the Barrett WAM to control the manipulator wrist. The online EKF trained network is able to adequately model the internal dynamics of a pan-tilt, while remaining stable due to the continuous learning. This is shown in both simulation and practice.
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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".