A guidance-based motion-planning methodology for the docking of autonomous vehicles
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Bibliographic record
Abstract
In this paper, a generic line-of-sight-sensing (LOS)-based guidance methodology is proposed for the docking of autonomous vehicles/robotic end-effectors: A multi-LOS task-space sensing system is used in conjunction with a guidance algorithm in a closed-loop feedback environment. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). In such instances, a guidance-based technique must be employed to move the vehicle to its desired pose using corrective actions at the final stages of its motion. Namely, after the vehicle/end-effector has failed to move to its desired docking pose within acceptable tolerances, LOS sensors initiate short-range corrective motion commands. The objective of the proposed guidance method is, thus, to successfully minimize the systematic errors of the vehicle, accumulated after a long-range motion, while allowing it to converge within the random noise limits. An additional advantage of the proposed system is its applicability to varying vehicle mobility requirements for high-precision docking. The proposed system was successfully tested via simulation on a 6 degree-of-freedom (DOF) vehicle. Numerous simulation tests of the behavior of the vehicle under the command of the guidance algorithm were conducted, one of which is presented herein. © 2005 Wiley Periodicals, Inc.
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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 | 0.001 | 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 it