Generatively optimised compact 3-DoF spherical parallel manipulator with tightly integrated actuators
Bibliographic record
Abstract
A singularity-free pose realization is a key feature for any robotic system. This paper presents the development of a 3-DoF pure rolling spherical parallel manipulator (SPM) with tightly integrated actuators. Conventional serial wrist mechanisms often face singularities and structural constraints, limiting performance during dynamic tasks. They also require more space due to their larger span. While parallel manipulators are compact, they often concentrate weight in a confined volume and offer limited room for actuator integration. To address these challenges, we propose a pure rolling spherical parallel manipulator (SPM) that incorporates generative design for weight optimization, reducing overall mass while preserving structural integrity and manipulability. This approach achieved a 54.61% reduction in linkage weight and a 33.9% reduction in total system-weight, enabling compact and efficient integration of direct or quasi-direct drive actuators. The mechanism’s unique topology allows pure rolling motion with decoupled yaw control, overcoming limitations of existing 2-DoF devices like Omni Wrist III. Using 3D printing for prototyping facilitated rapid iterations and testing. Performance evaluation demonstrated high precision, with mean positional errors of 0.0291 mm–0.2778 mm and orientation errors between 0.0562° and 0.3561°. Validated as a neck joint for an immersive teleoperated robot, the SPM shows promise in humanoid robotics and advanced manipulation requiring precise orientation control. The integration of actuators within a generatively optimized structure offers a compact, lightweight solution and a high-performance alternative for applications demanding singularity-free motion and reduced actuation-complexity. Future work will extend this design to higher payload systems using metal components, enhancing durability and functionality for industrial 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".