Neuromorphic Sensors with Visible Light Communications

M. Hulea, George-Iulian Uleru, Othman Isam Younus, S. Rajbhandari, Zabih Ghassemlooy
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引用次数: 2

Abstract

Spiking neural networks (SNN) can control single-joint robotic arms' precise rotation and force when shape memory alloy (SMA) actuators are used. SNN receives feedback from neuromorphic sensors for controlling anthropomorphic fingers, which typically respond to the flexion angle and the force applied to the fingertips. The robotic fingers and hands are in relative motion with the robot's body, which typically includes the main unit for the limb's motion control. An elegant method to implement the connection between the main control unit and the neuromorphic sensors on limbs is to use visible light communication technology. This work presents a system that uses the recently introduced optical axons to connect the neuromorphic force sensors placed on fingers and the main electronic SNN that controls the finger's motion. The SNN behaviour is evaluated comparatively with and without optical axons when the hand moves relatively to the neural control unit (NCU), which regulates the finger's force. The expected results show that despite small oscillations of the finger force during steady-state, the robotic hand can hold on to an object while moving in the vicinity of NCU.
具有可见光通信的神经形态传感器
当使用形状记忆合金作动器时,脉冲神经网络(SNN)可以控制单关节机械臂的精确旋转和受力。SNN接收来自控制拟人手指的神经形态传感器的反馈,拟人手指通常对弯曲角度和施加在指尖上的力做出反应。机器人的手指和手与机器人的身体相对运动,机器人的身体通常包括肢体运动控制的主要单元。实现主控制单元与四肢神经形态传感器之间连接的一种简便方法是使用可见光通信技术。这项工作提出了一个系统,它使用最近引入的光学轴突来连接放置在手指上的神经形态力传感器和控制手指运动的主要电子SNN。当手相对于调节手指受力的神经控制单元(NCU)运动时,比较有无光轴突时SNN的行为进行了评估。预期的结果表明,尽管在稳定状态下手指力有微小的振荡,但机器人手可以在NCU附近移动时抓住物体。
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