Bibliographic record
Abstract
Automated model generation and solution for motion planning and re-planning of robotic systems will play an important role in the future reconfigurable manufacturing systems. Solving the inverse kinematic problem has always been the key issue for computer-controlled robots. Considering the large amount of similarities that exist among the industrial 6R robotic systems, this work classifies them into two main types (Puma-type and Fanuc-type) and then provides a unified geometric solution based on a unified kinematic structure called Generic Puma-Fanuc (GPF) model. A widespread study of different kinematic groups originating from eleven robot manufacturers made it possible to develop the GPF model that can be reconfigured according to the D-H rules (Denavit, and Hartenberg1). A graphical interface by which the robot kinematic model is represented and the D-H parameters are auto-generated for use in solving the inverse kinematic problem. A generic solution module called Unified Kinematic Modeler and Solver (UKMS) implements the geometric approach for solving the inverse kinematic problem. The outcomes are then employed for robot control. Numerical examples are presented for exploring the solution capabilities of our unified approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".