Paper
6 October 1998 Sensor fusion for the navigation of an autonomous guided vehicle using neural networks
Jin Cao, Ernest L. Hall
Author Affiliations +
Abstract
A sensor fusion method for navigation of an Autonomous Guided Vehicle robot using Artificial Neural Network is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles and landmarks. The low-level sensor fusion technique is used for direct integration of sensor data, resulting in parameter and state estimates. The multi-layered perceptron, with a single hidden layer in neural network structure, and the back- propagation algorithm are employed for the mobile robot's navigation and for obstacle avoidance. The significance of this work lies in the development of a new estimation method for mobile robot obstacle avoidance and guidance.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jin Cao and Ernest L. Hall "Sensor fusion for the navigation of an autonomous guided vehicle using neural networks", Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); https://doi.org/10.1117/12.325774
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Sensors

Neural networks

Mobile robots

Sensor fusion

Control systems

Computer programming

Ultrasonics

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