Neural network-based target differentiation using sonar for robotics applications

Date
2000-08
Authors
Barshan, B.
Ayrulu, B.
Utete, S. W.
Advisor
Instructor
Source Title
IEEE Transactions on Robotics and Automation
Print ISSN
1042–296X
Electronic ISSN
Publisher
IEEE
Volume
16
Issue
4
Pages
435 - 442
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. The neural network can differentiate more targets with higher accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight (TOF) characteristics of these targets. Robustness tests indicate that the amplitude information is more crucial than TOF for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics.

Course
Other identifiers
Book Title
Keywords
Artificial neural networks, Evidential reasoning, Learning, Majority voting, Sensor data fusion, Sonar sensing, Target classification, Target differentiation, Target localization, Ultrasonic transducers.
Citation
Published Version (Please cite this version)