Kinematic Analysis, Control and Motion Planning of a Redundant (6+3)-DOF Hybrid Parallel Robot
Bibliographic record
Abstract
This paper introduces a (6+3)-DOF spatial hybrid parallel robot. It presents an innovative 3-DOF parallel wrist-gripper assembly, building upon the advancements highlighted in previous work, aiming to significantly enhance the base robot's capabilities. This assembly comprises a zero-torsion 2-DOF parallel wrist and a 1-DOF parallel gripper. The wrist enables a large singularity-free range of motion by generating a 2-DOF sphere-on-sphere pure rolling motion. This extensive singularity-free 2-DOF motion of the wrist facilitates seamless and precise manipulation of objects across various orientations, rendering it suitable for diverse applications such as assembly, pick-and-place, and inspection tasks. The paper leverages a geometric approach to derive analytical solutions for the robot's inverse kinematic problem. Validation of the derived inverse kinematics equations is carried out using MSC Adams. Additionally, a straightforward PD position control scheme is proposed for the control of the robot. Subsequently, a physical prototype is constructed, and experimental validation is conducted to demonstrate the effectiveness of the proposed controller. Overall, the (6+3)-DOF hybrid parallel robot proposed herein exhibits substantial potential for en-hancing the efficiency and adaptability of robotic manipulators across a broad spectrum of industrial and research-based applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".