Dynamic Manipulation and Stiffness Modulation of Cooperative Continuum Robots: Theory and Experiment
Bibliographic record
Abstract
Abstract Cooperative continuum robots (CCRs) are composed of multiple coupled continuum arms to cooperatively conduct manipulation tasks. They can highly enhance the performance of individual continuum arms by providing extra stiffness, leading to increased accuracy, payload capacity, and dynamic stability of the robot. This study aimed to investigate the stiffness analysis of tendon-driven supportive-type CCRs (S-CCRs). For this purpose, first, a generalized framework for the dynamic mathematical formulation and numerical solution of S-CCRs was proposed, their dynamic response to complex scenarios was obtained, and the accuracy of the model was experimentally evaluated. Then, the capability of stiffness modulation of S-CCRs was studied. Tendon-driven S-CCRs are potentially capable of changing the stiffness with structural configuration, providing active stiffness control at the design level. Hence, in this study, the effects of the connection point location/angle of the supportive arms to the operative arm, as well as the imposed tendon limitations of the supportive arm on the stiffness of the robot, and consequently on the dynamic payload manipulation, were studied and practical solutions were proposed to develop a simple but effective stiffness control mechanism. This study showed that a typical S-CCR can increase its stiffness, just by a modular connector design up to 84% during manipulation, bringing a novel opportunity for stiffness modulation of CCRs.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".