Additive Gaussian noise model and Kalman filter to enhance controllability of gesture-controlled teleoperated soft actuators
Bibliographic record
Abstract
Soft actuators are used in bilateral systems as slave-side actuators. To broaden the applications and usage of these soft actuators in teleoperated applications required high accuracy and control. This article focuses on the improvement and control of the soft actuators operated through the Internet using a glove-based gesture control system. A hand-worn sensor glove (a glove with flex sensors and accelerometers) is used to detect hand gestures (specifically bending angle and force generated in a finger while gripping a spherical object) of an operator. The operator’s finger movements are mimicked in a remote location, through the Internet, in a soft actuator. The bending angles of the human finger have converted into the pressure variations inside the actuator using a linear calibration technique. A vertically mounted actuator and a spherical object are used to demonstrate the gripping action. The main problems that occurred during the controlling of this setup are noise and delays. Electronic noise (line noise and circuit noise), mechanical noise (vibration, nonuniformities in actuator fabrication, wear, and tear), elastic effect (energy absorbed during the state transformation), communication delays (delays occurred due to geography, telecommunication infrastructure, and round trip transmission), and other noises (other environmental effects) degraded the performance. This article considered a Brownian motion model, an additive Gaussian noise model, and a Kalman filter to solve these problems. The experimentations are performed in three different locations (to demonstrate the teleoperation) and the recorded improvement in the performance is approximately 17%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".