Line-of-Sight Based 3D Localization of Parallel Kinematic Mechanisms
Bibliographic record
Abstract
Abstract Autonomous robots (manipulators or vehicles) may accumulate significant errors during their long-range motion to a desired position and orientation (pose). These errors, however, can be compensated for by subsequent local, short-range corrective actions to within random noise levels of the system. This paper presents a generic localization method for high-precision parallel kinematic mechanisms (PKMs) in order to allow them to accurately achieve their desired poses. The proposed method employs a novel non-contact spatial sensing technique combined with an iterative posecorrection procedure. The proposed sensing technique is based on the use of multiple spatial lines-of- sights (LOSs) emanating from a single source and ‘hitting’ a planar position sensitive detector (PSD) placed on the PKM’s platform. Using the positional feedback provided by the PSD, the instantaneous actual pose of the platform is accurately estimated. A pose-correction method is subsequently invoked to iteratively guide the platform to its desired location within noise levels. Extensive simulations were carried out to illustrate the effectiveness of the proposed localization method for a spatial PKM being developed in our laboratory.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".