Obstacle avoidance with LGMD neuron: Towards a neuromorphic UAV implementation

Abstract

We present a neuromorphic adaptation of a spiking neural network model of the locust Lobula Giant Movement Detector (LGMD), which detects objects increasing in size in the field of vision (looming) and can be used to facilitate obstacle avoidance in robotic applications. Our model is constrained by the parameters of a mixed signal analog-digital neuromorphic device, developed by our group, and is driven by the output of a neuromorphic vision sensor. We demonstrate the performance of the model and how it may be used for obstacle avoidance on an unmanned areal vehicle (UAV).