Kinematic properties and control for reconfigurable robotic system
Bibliographic record
Abstract
This paper presents the kinematic aspects of reconfigurable manipulators such as workspace, manipulability and singularity properties. These properties are calculated and shown in 3D figures. Variable control structures are designed for reconfigurable robotic systems. These robotic 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 ABB, Stanford 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 robotic system hence encompasses different kinematic structures and has a reconfigurable joint to accommodate any required application in medical technology, space exploration and future manufacturing systems among others. A 3-DOF reconfigurable robotic system is constructed and demonstrated as a case study which covers all possible open kinematic structures. SMC controllers were developed based on estimated, bounded models and on computed torque method. This research is intended to serve as a foundation for future studies in reconfigurable control systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".