Deep Neural Network Approach In Electrical Impedance Tomography-Based Real-Time Soft Tactile Sensor
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)(2019)
摘要
Recently, a whole-body tactile sensing have emerged in robotics for safe human-robot interaction. A key issue in the whole-body tactile sensing is ensuring large-area manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in large-area tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwell's equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EIT-based tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabric-based soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.
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关键词
Deep Neural Network approach,electrical impedance tomography-based real-time soft tactile sensor,whole-body tactile sensing,human-robot interaction,large-area manufacturability,reconstruction method,large-area tactile sensing,voltage measurements,measurement electrodes,computation time,reconstruction accuracy,novel nonlinear EIT algorithm,EIT-based tactile sensors,neural network architecture,rectified linear unit function,extremely low computational time,nonlinear network structure,additive Gaussian noise,actual measurement noise,sensor application,trained DNN model,conductive fabric-based soft tactile sensor,reconstruction error,noise robustness,conventional linearized model
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