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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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A Real-Time Dynamic Simulator and an Associated Front-End Representation Format for Simulating Complex Robots and Environments
Robot Dynamic Simulators offer convenient implementation and testing of physical robots, thus accelerating research and development. While existing simulators support most real-world robots with serially linked kinematic and dynamic chains, they offer limited or conditional support for complex closed-loop robots. On the other hand, many of the underlying physics computation libraries that these simulators employ support closed-loop kinematic chains and redundant mechanisms. Such mechanisms are often utilized in surgical robots to achieve constrained motions (e.g., the remote center of motion (RCM)). To deal with such robots, we propose a new simulation framework based on a front-end description format and a robust real-time dynamic simulator. Although this study focuses on surgical robots, the proposed format and simulator are applicable to any type of robot. In this manuscript, we describe the philosophy and implementation of the front-end description format and demonstrate its performance and the simulator's capabilities using simulated models of real-world surgical robots.
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- Award ID(s):
- 1637759
- PAR ID:
- 10207704
- Date Published:
- Journal Name:
- 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 1875 to 1882
- 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
- Conference Paper:
- https://doi.org/10.1109/IROS40897.2019.8968568
