Forward Kinematic Analysis of Kinematically Redundant Hybrid Parallel Robots
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper focuses on the forward kinematic analysis of (6 + 3)-degree-of-freedom kinematically redundant hybrid parallel robots. Because all of the singularities are avoidable, the robot can cover a very large orientational workspace. The control of the robot requires the solution of the direct kinematic problem using the actuator encoder data as inputs. Seven different approaches of solving the forward kinematic problem based on different numbers of extra encoders are developed. It is revealed that five of these methods can produce a unique solution analytically or numerically. An example is given to validate the feasibility of these approaches. One of the provided approaches is applied to the real-time control of a prototype of the robot. It is also revealed that the proposed approaches can be applied to other kinematically redundant hybrid parallel robots proposed by the authors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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