Tele-Operable Controlling System for Hand Gesture Controlled Soft Robot Actuator
Bibliographic record
Abstract
This paper focuses on investigating the delays of a tele-operable bilateral system design with a soft actuator. The setup we have used is governed by realizing control signals obtained from hand gestures through the Internet, over a long distance. The control signals are necessary to be received promptly to the controller and lags are important to minimize in establishing real time control of the system. Slave side is a soft actuator made out of an elastomer material which is controlled by pneumatic actuation. Solenoid valves are used to control the actuator. Through out the experimentation, we have assumed the actuator to follow the Neo Hookean (hyper-elastic model) behavior. A data glove is used to realize the bending angle and forces generated during soft grasping by the operator's finger. Then these control signals are transmitted to the slave side using Internet. These signals are used to control the air pressure of the cavities in soft actuator. A closed loop system is established by attaching a flex sensor in the soft actuator. Message Queuing Telemetry Transport (MQTT) server is used to deliver the data packets which has control data. Several experimentations are carried out in different geographical locations to study the behavior and delays associated with the setup. We have mainly encountered delays due to communication (mainly Round Trip Delay Time (RTT)), elastic saturation and minor delays due to various other factors. This paper studies and discusses delays and their possible causes. We have compared the elastic saturation delays obtained by the finite element model and delays measured by experiments. Final results of the study revealed the importance of considering these delay factors in design level to establish real-time control in soft actuator systems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".