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Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot’s 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot’s configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics.
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Fault Detection and Response for Safe Control of Artificial Muscles in Soft Robots
Robots built from soft materials have the potential for intuitively-safer interactions with humans and the environment. However, soft robots’ embodiments have many sources of failure that could lead to unsafe conditions in closed-loop control, such as degradation of sensors or fracture of actuators. This letter proposes a fault detection system for sensors attached to artificial muscle actuators that satisfies a formal safety condition. Our approach combines redundant sensing, model-based state estimation, and Gaussian process regression to determine when one sensor’s reading statistically diverges from another, indicating a fault condition. We apply the approach to electrothermal shape memory alloy (SMA) artificial muscles, demonstrating that our method prevents the overheating and fire damage risk that could otherwise occur. Experiments show that when the muscle’s nominal sensor (temperature via a thermocouple) is fractured from the robot, the redundant sensor (electrical resistance) combined with our method prevents violation of state constraints. Deploying this system in real-world human-robot interaction could help make soft robots more robust and reliable.
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- PAR ID:
- 10654847
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Control Systems Letters
- Volume:
- 9
- ISSN:
- 2475-1456
- Page Range / eLocation ID:
- 2321 to 2326
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LCSYS.2025.3610637
