Computation of Inverse Kinematics of Redundant Manipulator Using Particle Swarm Optimization Algorithm and Its Combination with Artificial Neural Networks
Bibliographic record
Abstract
The industry heavily relies on robotic manipulators at present. Despite their age, recent methodologies have significantly advanced their functionality, integrating them into daily life. Rescue robots are popular. However, their precision and reaction speed are the main issues for real-world rescuing. This research aims to enhance the precision of rescue robotic manipulators’ end-effectors in real time. It achieves this by deriving and solving inverse kinematics formulations for 2-, 3-, and 4-link manipulators using Particle Swarm Optimization (PSO). The PSO method proves highly accurate, outperforming previous studies utilizing Artificial Neural Networks (ANNs). While PSO requires more time than ANNs, a hybrid approach, PSO-ANN, balances accuracy and speed, offering real-time solutions with minimal errors, and contributing to a precise methodology for real-time robotic manipulator operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".