A Test-Bed for Visual Servo Control of Artificial Muscle Micro-Robot with Parallel Architecture
Bibliographic record
Abstract
Artificial muscles, or soft smart materials, are being increasingly used to build micro-manipulators. Electroactive polymer (EAP) actuators, including electronic EAP and ionic EAP, are attracting interest from research community because of their potential to achieve high strains and therefore high displacements. However, their application is limited by their ability to generate large forces. A parallel architecture, i.e. handling objects using multiple polymer actuators, can greatly enhance their load carrying ability. A significant technical challenge presented by micro-manipulators based on artificial muscle actuators is the nonlinearity of actuator dynamics. Currently, there is no satisfactory model that can used in the control design. Visual servo (VS) control of the displacements of a robot in closed-loop using the data provided by one or multiple cameras is presented as a possible approach to this problem. A parallel architecture micro-gripper is designed as a prototype of a micro-gripper. A vision system is developed to identify dynamic behaviors of IPMC arms and design a control system for the parallel micro-gripper. Preliminary identification and control results are given. Visual servoing motion control is introduced
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".