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Dynamic liquid volume estimation using optical tactile sensors and spiking neural network

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Abstract

Tactile sensing plays a unique role in robotic applications such as liquid volume estimation. The latest research shows that current computer vision methods for liquid volume estimation are still unsatisfactory in terms of accuracy. Additionally, their performance depends on the environment, such as requiring high illumination or facing issues with occlusions. In the face of these challenges, we design an optical tactile sensor to solve the task of liquid volume estimation. We build the sensor using four photoresistors and a LED on a printed circuit board, and an elastic dome bound with a 3D-printed base by four screw suits. The tactile sensor is attached to the Robotiq and applied in the task of liquid volume estimation in different trajectories movement. The experiment is designed as multiple different trajectories for imitating human movements. We propose the tactile regression spiking neural network (TR-SNN) for the liquid volume estimation. The TR-SNN uses a binary decoding method to decode the output spike trains of the network into liquid volume values. It is a novel and successful spiking neural network (SNN) decoding method for the liquid volume estimation task. The experimental results show that compared to baseline models, the TR-SNN can achieve a state-of-the-art estimation performance with a coefficient of determination up to 0.986.

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Acknowledgements

This work was supported in part by National Natural Science Foundation for Youth of China under Grant 62203428, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110486, in part by the Shenzhen Science and Technology Program under Grant RCBS20210706092252054, and in part by the Science and Technology Innovation Commission of Shenzhen (No. JSGGZD20220822095401004), Postdoctoral Science Foundation of China (No. 2021M703389).

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Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, 518055, China

    Binhua Huang, Senlin Fang, Meng Yin, Zhengkun Yi, Chaoxiang Ye, Xiaoyu Li, Zhenning Zhou & Xinyu Wu

  2. City University of Macau, Avenida Padre Tomás Pereira, Taipa, Macau, China

    Senlin Fang

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  1. Binhua Huang
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Correspondence to Meng Yin or Zhengkun Yi.

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Huang, B., Fang, S., Yin, M. et al. Dynamic liquid volume estimation using optical tactile sensors and spiking neural network. Intel Serv Robotics 17, 345–355 (2024). https://doi.org/10.1007/s11370-023-00488-0

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