1. Collision Detection in Complex Dynamic Scenes Using an LGMD-Based Visual Neural Network With Feature Enhancement.
- Author
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Shigang Yue and Rind, F. Claire
- Subjects
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DETECTORS , *NEURONS , *ALGORITHMS , *ROBOTICS , *ARTIFICIAL neural networks , *MOBILE robots - Abstract
The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by back- ground detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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