Design of Lightweight and Extensible Tendon-Driven Continuum Robots using Origami Patterns
Bibliographic record
Abstract
Tendon-driven continuum robots (TDCR) have been researched to utilize their slender structure, compliance, large workspace, and follow-the-leader deployment capabilities. Improving the performances of TDCR by increasing its length and extensibility is however challenging due to the need of guiding the tendons along the backbone. A standard design uses rigid spacer disks, which mass may cause stability issues in the case of robots with long length. In this paper, we propose a design of lightweight and extensible TDCR taking advantage of extensible paper-based origami structures to guide the tendons along a superelastic nitinol backbone. The four tendons and the backbone control the three degrees of freedom of the robot, yaw, pitch, and axial translation. The robot is composed of multi-segments, and the manufacturing process with water-based ink printing provides fast, easily customizable, and scalable fabrication of the robots. The design allows for a reduction of up to 95% of the robot mass with respect to a standard design of TDCR. The prototype demonstrates the extension ratio of more than 10 times and ±167 degrees yaw/pitch angle with 21.9 g weight. The proposed robot design can be applied for search and rescue missions and minimally invasive surgical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".