Neon drive robot3/15/2023 In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. In this paper, we present an infrastructure upgrade to the ED-Scorbot platform, replacing the controller hardware, which was comprised of two Spartan Field Programmable Gate Arrays (FPGAs) and a barebone computer, with an edge device, the Xilinx Zynq-7000 SoC (System on Chip) which reduces the response time, power consumption and overall complexity. The Event-Driven Scorbot platform (ED-Scorbot) consists of a 6 Degrees of Freedom (DoF) robotic arm whose controller implements a Spiking Proportional-Integrative- Derivative algorithm, mimicking in this way the previously commented biological systems. As opposed to human-created encoding systems, neural ones use neurons and spikes to process the information and make weighted decisions based on a continuous learning process. In addition, biological systems are able to respond to diverse stimuli rather fast and efficiently, and this is based on the way information is coded within neural processes. Biological muscle movement consists of stretching and shrinking fibres via spike-commanded signals that come from motor neurons, which in turn are connected to a central pattern generator neural structure. In this work, an all-in-one neuromorphic controller system with reduced latency and power consumption for a robotic arm is presented.
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