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Proceedings Paper

Neural network-based landmark detection for mobile robot
Author(s): Minoru Sekiguchi; Hiroyuki Okada; Nobuo Watanabe
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Paper Abstract

The mobile robot can essentially have only the relative position data for the real world. However, there are many cases that the robot has to know where it is located. In those cases, the useful method is to detect landmarks in the real world and adjust its position using detected landmarks. In this point of view, it is essential to develop a mobile robot that can accomplish the path plan successfully using natural or artificial landmarks. However, artificial landmarks are often difficult to construct and natural landmarks are very complicated to detect. In this paper, the method of acquiring landmarks by using the sensor data from the mobile robot necessary for planning the path is described. The landmark we discuss here is the natural one and is composed of the compression of sensor data from the robot. The sensor data is compressed and memorized by using five layered neural network that is called a sand glass model. The input and output data that neural network should learn is the sensor data of the robot that are exactly the same. Using the intermediate output data of the network, a compressed data is obtained, which expresses a landmark data. If the sensor data is ambiguous or enormous, it is easy to detect the landmark because the data is compressed and classified by the neural network. Using the backward three layers, the compressed landmark data is expanded to original data at some level. The studied neural network categorizes the detected sensor data to the known landmark.

Paper Details

Date Published: 22 March 1996
PDF: 8 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235913
Show Author Affiliations
Minoru Sekiguchi, Fujitsu Labs. Ltd. (Japan)
Hiroyuki Okada, Fujitsu Labs. Ltd. (Japan)
Nobuo Watanabe, Fujitsu Labs. Ltd. (Japan)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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