Sensor fusion for the navigation of an autonomous guided vehicle using neural networks

Jin Cao; Ernest L. Hall

doi:10.1117/12.325774

6 October 1998 Sensor fusion for the navigation of an autonomous guided vehicle using neural networks

Jin Cao, Ernest L. Hall

Proceedings Volume 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision; (1998) https://doi.org/10.1117/12.325774
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

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.

Citation Download Citation

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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