Reconfiguration Analysis of a Fully Reconfigurable Parallel Robot
Bibliographic record
Abstract
This paper presents a new method for the combined topological and geometric reconfiguration of a parallel robot to achieve task-based reconfiguration. Using the existing structure of a six degree-of-freedom (DOF) parallel robot, reconfiguration to limited mobility modes, a configuration with less than six degrees-of-freedom, can be achieved easily without the need to remove branches from the robot structure. Branch modules are instead, reconfigured from an unconstrained-active to a constrained-passive state by means of hybrid active/passive motors and reconfigurable universal-to-revolute joints. In doing so, the robot is capable of assuming a configuration in which the number of task-based degrees-of-freedom match the number of controllable actuators within the robot structure. The selection of branch modules for reconfiguration is independent of the limited mobility mode required and leads to multiple isomorphic configurations. A comparative study is thus needed to understand not only the implication of morphing, but also the capabilities of the reconfigured robot. For this purpose, a branch-based mobility analysis is performed and isomorphic configurations are identified. These isomorphic configurations are then compared based on their workspace and kinematic capabilities for which a parametric kinematic constraint formulation is developed. The comparative study evaluates the abilities of each configuration and is used for guidance in selecting an appropriate configuration for a particular task. The developed tools can also be used for design evaluation purposes.
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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".