Authors:
Ana Silva
;
Jorge Silva
and
Cristina Santos
Affiliation:
University of Minho, Portugal
Keyword(s):
Bio-inspired Model, Lobula Giant Movement Detector Neuron, Artificial Neural Networks, Collision Avoidance.
Related
Ontology
Subjects/Areas/Topics:
Decision Support Systems
;
Engineering Applications
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
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.