Kinematics and Path Planning of a Six-Degrees-of-Freedom Robot Manipulator
Bibliographic record
Abstract
This paper introduces a strategy to accomplish pick-and-place operations for a six-degrees-of-freedom (6-DOF) robotic arm attached to a wheeled mobile robot. This research work is part of a bigger project in developing a robotic-assisted nursing to be used in medical settings. The significance of this project relies on the increasing demand for elderly and disabled skilled care assistance which nowadays has become insufficient. Several methods were implemented to make a 6-DOF manipulator capable of performing pick-and-place operations. This paper presents an approach for solving the inverse kinematics problem and planning collision-free paths. An Iterative Inverse Kinematics method (IIK) was introduced to find multiple configurations for the manipulator along a given path. The IIK method takes advantage of a specific geometric characteristic of the manipulator, in which several joints share a common plane. Ten different scenarios with different number and pattern of obstacles were used to verify the efficiency of a path planning algorithm introduced here. Other methods, also implemented in the current project, which describe the manipulator and its capabilities, are presented elsewhere [1]. Overall results confirmed the efficiency of the implemented methods for performing pick-and-place operations for a 6-DOF manipulator.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".