International Journal of Circuit Theory and Applications · 2002 · 13 citations · 14 references
Artificial IntelligenceEngineeringNeural Networks (Machine Learning)Intelligent RoboticsAutonomous Vehicle NavigationIntelligent SystemsSocial SciencesVisual NavigationMobile RobotNecessary Signal ProcessingMobile RoboticsRobot LearningRobotics PerceptionCellular Neural NetworksMachine VisionVision RoboticsComputer ScienceNeural Networks (Computational Neuroscience)Autonomous NavigationComputer VisionRobot ControlCellular Neural NetworkRobotics
Abstract We show how cellular neural networks (CNNs) are capable of providing the necessary signal processing needed for visual navigation of an autonomous mobile robot. In this way, even complex feature detection and object recognition can be obtained in real time by analogue hardware, making fully autonomous real‐time operation feasible. An autonomous robot was first simulated and then implemented by simulating the CNN with a DSP. The robot is capable of navigating in a maze following lines painted on the floor. Images are processed entirely by a CNN‐based algorithm, and navigation is controlled by a fuzzy‐rule‐based algorithm. Copyright © 2002 John Wiley & Sons, Ltd.
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