eMorph: Towards Neuromorphic Robotic Vision

CVPR 2011 WORKSHOPS, 2011

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2013 IEEE International Autumn Meeting on Power Electronics and Computing (ROPEC), 2013

Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach.

Lecture Notes in Computer Science, 2014

IEEE Access

ArXiv, 2021

Many animals meander in environments and avoid collisions. How the underlying neuronal machinery can yield robust behaviour in a variety of environments remains unclear. In the fly brain, motion-sensitive neurons indicate the presence of nearby objects and directional cues are integrated within an area known as the central complex. Such neuronal machinery, in contrast with the traditional stream-based approach to signal processing, uses an event-based approach, with events occurring when changes are sensed by the animal. Contrary to classical von Neumann computing architectures, event-based neuromorphic hardware is designed to process information asynchronously and in a distributed manner. Inspired by the fly brain, we model, for the first time, a neuromorphic closed-loop system mimicking essential behaviours observed in flying insects, such as meandering in clutter and crossing of gaps, both of which are also highly relevant for autonomous vehicles. We implemented our system both i...

2020