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Towards neuromorphic FPGA-based infrastructures for a robotic arm

Towards neuromorphic FPGA-based infrastructures for a robotic arm

Authors :
Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores
Ministerio de Ciencia e Innovación (MICIN). España
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
European Union (UE). H2020
Canas Moreno, Salvador
Piñero Fuentes, Enrique
Ríos Navarro, José Antonio
Cascado Caballero, Daniel
Pérez-Peña, Fernando
Linares Barranco, Alejandro
Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores
Ministerio de Ciencia e Innovación (MICIN). España
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
European Union (UE). H2020
Canas Moreno, Salvador
Piñero Fuentes, Enrique
Ríos Navarro, José Antonio
Cascado Caballero, Daniel
Pérez-Peña, Fernando
Linares Barranco, Alejandro
Publication Year :
2023

Abstract

Muscles are stretched with bursts of spikes that come from motor neurons connected to the cerebellum through the spinal cord. Then, alpha motor neurons directly innervate the muscles to complete the motor command coming from upper biological structures. Nevertheless, classical robotic systems usually require complex computational capabilities and relative high-power consumption to process their control algorithm, which requires information from the robot’s proprioceptive sensors. The way in which the information is encoded and transmitted is an important difference between biological systems and robotic machines. Neuromorphic engineering mimics these behaviors found in biology into engineering solutions to produce more efficient systems and for a better understanding of neural systems. This paper presents the application of a Spike-based Proportional-Integral-Derivative controller to a 6-DoF Scorbot ER-VII robotic arm, feeding the motors with Pulse-Frequency-Modulation instead of Pulse-Width-Modulation, mimicking the way in which motor neurons act over muscles. The presented frameworks allow the robot to be commanded and monitored locally or remotely from both a Python software running on a computer or from a spike-based neuromorphic hardware. Multi-FPGA and single-PSoC solutions are compared. These frameworks are intended for experimental use of the neuromorphic community as a testbed platform and for dataset recording for machine learning purposes.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1442718940
Document Type :
Electronic Resource