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An Optical Curvature Sensor for Soft Robots
2022For precise control of soft robots soft sensors are required to measure the current configuration of the robot. In this contribution a new simple, accurate and easy to fabricate soft optical curvature sensors is presented. The optical sensor is based on the light loss along several polymer optical cable (POF) pieces which are lined up.
Grube, Malte, Seifried, Robert
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IEEE Transactions on Industrial Informatics
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly improved sensing accuracy; however, their vulnerability to adversarial attacks affects their reliability, thus hindering their widespread application. To improve the
Runyuan Guo, Han Liu, Ding Liu
semanticscholar +1 more source
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly improved sensing accuracy; however, their vulnerability to adversarial attacks affects their reliability, thus hindering their widespread application. To improve the
Runyuan Guo, Han Liu, Ding Liu
semanticscholar +1 more source
IEEE Transactions on Industrial Informatics
Soft sensing using the neural network technique has been increasingly applied to industrial processes. Recently, the security and robustness of neural network-based soft sensors have become primary concerns.
Lei Chen, Qun Zhu, Yanlin He
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Soft sensing using the neural network technique has been increasingly applied to industrial processes. Recently, the security and robustness of neural network-based soft sensors have become primary concerns.
Lei Chen, Qun Zhu, Yanlin He
semanticscholar +1 more source
Reliable Soft Sensors With an Inherent Process Graph Constraint
IEEE Transactions on Industrial InformaticsNowadays, data-driven models have been prevalent in predicting hard-to-measure key quality indicators of industrial processes in order to improve product quality and process safety.
Ruikun Zhai +3 more
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Soft tactile sensor and curvature sensor for caterpillar-like soft robot's adaptive motion
Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence, 2019Soft robots are designed to interact safely with their environment and move through narrow place in a way a rigid robot cannot. In order for the soft robot to be completely soft, it should consist of as more as soft component. In this paper, a PneuNet soft robot imitating bionic caterpillar are fabricated.
Xin Yuan +4 more
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Sensor Embedded Soft Fingertip for Precise Manipulation and Softness Recognition
IEEE Robotics and Automation Letters, 2021Up to this day, precise manipulation still relies on human hands, which benefits from the rich neural sensing in the fingertips. For robotic hands, force perception, recognizing the softness of objects, and detecting slip can significantly improve the grasping and manipulating abilities.
Xiaolong Ma +5 more
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3D printing of soft sensors for soft gripper applications
Materials Today: Proceedings, 2022Agency for Science, Technology and Research (A*STAR)
Goh, Guo Liang +4 more
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Soft Sensor Network for Environmental Monitoring
2016This paper shows the application of a soft sensor network for the detection of meteorological events. A set of hard (real) sensor are placed in a territory, where they measure heterogeneous quantities. Starting from their measurements, a soft sensor network provides useful information coming from the data.
Maniscalco U, Pilato G, Vella F
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3D printed resistive soft sensors
2018 IEEE International Conference on Soft Robotics (RoboSoft), 2018Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g. geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors are effective for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve ...
Benjamin Shih +4 more
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Input selection methods for data-driven Soft sensors design: Application to an industrial process
Information Sciences, 2020Soft Sensors (SSs) are inferential models which are widely used in industry. They are generally built through data-driven approaches that exploit industry historical databases. Selection of input variables is one of the most critical issues in SSs design.
F. Curreri, S. Graziani, M. Xibilia
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