Triggering Robot Hand Reflexes with Human EMG Data Using Spiking Neurons

2018 ◽  
pp. 902-916 ◽  
Author(s):  
J. Camilo Vasquez Tieck ◽  
Sandro Weber ◽  
Terrence C. Stewart ◽  
Arne Roennau ◽  
Rüdiger Dillmann
Keyword(s):  
Spiking Neurons ◽  
Robot Hand

Design of an underactuated robot hand based on displacement-force conversion mechanism

2012 ◽  
Vol 13 (4) ◽  
pp. 509-516 ◽  
Author(s):  
Hun-Keon Ko ◽  
Chang-Hee Cho ◽  
Hyo-Chan Kwon ◽  
Kwon-Hee Kim
Keyword(s):  
Robot Hand ◽  
Displacement Force ◽  
Underactuated Robot

Stable In-Grasp Manipulation with a Low-Cost Robot Hand by Using 3-Axis Tactile Sensors with a CNN

2020 ◽  
Author(s):  
Satoshi Funabashi ◽  
Tomoki Isobe ◽  
Shun Ogasa ◽  
Tetsuya Ogata ◽  
Alexander Schmitz ◽  
...  
Keyword(s):  
Low Cost ◽  
Robot Hand ◽  
Tactile Sensors

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

A Three-Fingered Robot Hand Based on the Slider and Rocker Mechanism

2021 ◽  
Author(s):  
Lei Zhang ◽  
Tianyi Zhang ◽  
Xuguang Wang ◽  
Xingtian Yao ◽  
Dan Zhang
Keyword(s):  
Robot Hand

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

2021 ◽  
Vol 71 ◽  
pp. 102136
Author(s):  
Mingyang Li ◽  
Zhijiang Du ◽  
Xiaoxing Ma ◽  
Wei Dong ◽  
Yongzhuo Gao
Keyword(s):  
3D Reconstruction ◽  
Calibration Method ◽  
Laser Sensor ◽  
Robot Hand
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