Self-Configuration Fuzzy System for Inverse Kinematics of Robot Manipulators
Bibliographic record
Abstract
Kinematics is the study of motion without regard to the forces that create it. Generally, kinematics for robot manipulators includes two problems, forward kinematics problem and inverse kinematics problem. Because of the complexity of inverse kinematics, it is difficult to find the solutions for it. This paper applies a self-configuration fuzzy system to finding the solutions for inverse kinematics of robot manipulators. In this paper, the problem of fuzzy approach for inverse kinematics is described first. Then a self-configuration fuzzy system is introduced. Based on a small group of given input-output pairs selected by covering its workspace, an initially simple fuzzy model for inverse kinematics is built, including some basic rules, the number and the parameters of membership functions. Then, by applying given input-output data pairs to this simple fuzzy model, approximating error can be calculated. Consequently, the optimum rule conclusion, optimized membership functions, and new structure can be obtained. Furthermore, an overall analysis in the domain of the whole function is carried out instead of concentrating on the subspace. After optimization problems are solved, a fuzzy system is well defined to solve the inverse kinematics. Finally, the simulation verifies this self-configuration fuzzy system for inverse kinematics of robot manipulators
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".