Locking Force and Stiffness Oriented Design for an SMA-Actuated Miniaturized Lockable Prismatic Joint
Bibliographic record
Abstract
Abstract Lockable mechanisms offer significant advantages for robotic systems, such as enabling effective energy management, motion reconfiguration, and stiffness adjustment. Crucially, when unlocked, these mechanisms allow the robot’s intended motion to proceed unimpeded. Upon locking, however, they enable motion reconfiguration and provide substantially enhanced load-bearing capacity (with increased stiffness). This capability allows them to be seamlessly integrated into existing robotic systems. In this study, we propose a novel lockable prismatic (P) joint that is modular, miniaturized, and capable of high load-bearing with high stiffness, based on compliant mechanisms and shape memory alloy (SMA) actuators. We first detail the joint's working principle and identify critical design parameters governing its locking performance and stiffness. Subsequently, we present an optimized design framework, illustrated with two design cases. Experimental validation confirms the joint's functionality, achieving a locking force of up to 180 N and a locked-state axial stiffness of 1400 N/mm. Furthermore, we demonstrate the joint’s practical utility through its application in a motion-reconfigurable, snake-like robotic arm with a compact design space and multiple motion modes. The arm can navigate into confined spaces like wing boxes using diverse motion modes and can lock into a high-stiffness configuration for stable end-loaded operations. Collectively, this research illuminates a pathway towards utilizing smart materials and compliant mechanisms to create high-performance lockable P joints, providing a locking and motion reconfiguration solution that is easy to design and use for robots of different sizes and load-carrying capabilities.
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 | 0.001 | 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".