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Obstacle avoidance with LGMD neuron: towards a neuromorphic UAV implementation


Salt, Llewyn; Indiveri, Giacomo; Sandamirskaya, Yulia (2017). Obstacle avoidance with LGMD neuron: towards a neuromorphic UAV implementation. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2017, Baltimore, USA, 29 May 2017 - 6 January 2017.

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).

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).

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:6 January 2017
Deposited On:23 Feb 2018 09:23
Last Modified:31 Jul 2018 05:12
Publisher:Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS) 2017
Series Name:IEEE International Symposium on Circuits and Systems, ISCAS
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/ISCAS.2017.8050976
Official URL:http://ieeexplore.ieee.org/abstract/document/8050976/

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