Towards neuromorphic FPGA-based infrastructures for a robotic arm
Muscles are stretched with bursts of spikes that come frommotor 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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::b9d3fb5a6571a0a091bb97d3523c739b |
| Acceso en línea: | https://hdl.handle.net/11441/185589 https://doi.org/10.1007/s10514-023-10111-x |
| Access Level: | acceso abierto |
| Palabra clave: | Neuromorphic engineering Spike-based motor control FPGA Robotic arm |
| Sumario: | Muscles are stretched with bursts of spikes that come frommotor 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 highpower 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 themotorswith 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. |
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