An ANN and Virtual Vector Function Approach for the Computation of the Inverse Kinematics of Redundant Manipulators
Bibliographic record
Abstract
This Paper presents an Artificial Neural Network (ANN) method for the solution of the Inverse Kinematics (IK) of redundant manipulators. This problem normally has an infinite number of solutions. Several conventional approaches based on numerical methods have been proposed over the years. However, it is well known that ANN implementations for the solution of the problem for redundant manipulators are inefficient since they lead to inaccurate solutions. The main issue on the implementation of ANN solutions is that an attempt is made on mapping a relation from a given number of variables in the manipulator task space to a larger number of variables in the joint space. This Paper proposes the inclusion of a virtual vector function in the task space to “complete” it; so that the number of variables in the task space is at least equal to the number of variables in the joint space. Here, the proposed approach is successfully tested on a 3 DOF planar redundant manipulator performing four diverse target trajectories inside the manipulator’s workspace. Additionally, in order to define the target trajectories, some manipulator’s links limitations are considered and some conditions are set for the target trajectories.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".