Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/25306

TitleLGMD based neural network for automatic collision detection
Author(s)Silva, Ana
Silva, Jorge Bruno
Santos, Cristina
KeywordsBio-inspired model
Lobula Giant Movement Detector neuron
Artificial neural networks
Collision avoidance
Issue date2012
PublisherSCITEPRESS
Abstract(s)Real-time collision detection in dynamic scenarios is a hard task if the algorithms used are based on conventional techniques of computer vision, since these are computationally complex and, consequently, time-consuming. On the other hand, bio-inspired visual sensors are suitable candidates for mobile robot navigation in unknown environments, due to their computational simplicity. The Lobula Giant Movement Detector (LGMD) neuron, located in the locust optic lobe, responds selectively to approaching objects. This neuron has been used to develop bio-inspired neural networks for collision avoidance. In this work, we propose a new LGMD model based on two previous models, in order to improve over them by incorporating other algorithms. To assess the real-time properties of the proposed model, it was applied to a real robot. Results shown that the LGMD neuron model can robustly support collision avoidance in complex visual scenarios.
TypeConference paper
URIhttp://hdl.handle.net/1822/25306
ISBN9789898565211
Peer-Reviewedyes
AccessOpen access
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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