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dc.contributor.authorSilva, Ana-
dc.contributor.authorSilva, Jorge Bruno-
dc.contributor.authorSantos, Cristina-
dc.description.abstractReal-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.por
dc.subjectBio-inspired modelpor
dc.subjectLobula Giant Movement Detector neuronpor
dc.subjectArtificial neural networkspor
dc.subjectCollision avoidancepor
dc.titleLGMD based neural network for automatic collision detectionpor
oaire.citationConferenceDate28 - 31 Jul. 2012por
oaire.citationConferencePlaceRome, Italypor
oaire.citationTitle9th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2012)por
sdum.conferencePublication9th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2012)por
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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