Analysis and development of self-reconfigurable open kinematic machinery systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the analysis and development of the model, dynamics and control of new self-reconfigurable machinery systems. These machinery systems combine as many properties of different open kinematic structures as possible and can be used for a variety of applications. The kinematic design parameters, i.e., their Denavit-Hartenberg (D-H) parameters, can be modified to satisfy any configuration required to meet a specific task. By varying the joint twist angle parameter (configuration parameter), the presented model is reconfigurable to any desired open kinematic structure, such as Fanuc, ABB and SCARA robotic systems. The joint angle and the offset distance of the D-H parameters are also modeled as variable parameters (reconfigurable joint). The resulting self-reconfigurable machinery system hence encompasses different kinematic structures and has a reconfigurable joint to accommodate any required application. Using the Newton-Euler (N-E) recursive approach, the dynamic parameters of a reconfigurable joint are calculated and presented. A nonlinear control law is developed for a general reconfigurable joint using Lyapunov second method achieving asymptotic stability and the required performance objectives. Automatic model generation of a 3-DOF reconfigurable machinery system is constructed and demonstrated as a case study which covers all possible open kinematic structures. This research is intended to serve as a foundation for future studies in reconfigurable control systems.
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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) | 0.000 | 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) | 0.001 | 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 it