Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Publication date | 2/05/2007 |
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Host publication | Mobile Roots: The Evolutionary Approach |
Editors | Nadia Nedjah, Luiza Macedo Mourelle, Leandro Santos Coelho |
Pages | 89-118 |
Number of pages | 30 |
<mark>Original language</mark> | English |
Name | Studies in Computational Intelligence |
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Volume | 50 |
ISSN (Print) | 1860-949X |
In this chapter, an approach to real-time landmark recognition and simultaneous classifier design for mobile robotics is introduced. The approach is based on the recently developed evolving fuzzy systems (EFS) method [1], which is based on subtractive clustering method [2] and its on-line evolving extension called eClustering [1]. When the robot travels in an unknown environment, the landmarks are automatically deteced and labelled by the EFS-based self-organizing classifier (eClass) in real-time. It makes fully autonomous and unsupervised joint landmark detection and recognition without using the absolute coordinates (altitude or longitude), without a communication link or any pretraining. The proposed algorithm is recursive, non-iterative, incremental and thus computationally light and suitable for real-time applications. Experiments carried out in an indoor environment (an office located at InfoLab21, Lancaster University, Lancaster, UK) using a Pioneer3 DX mobile robotic platform equipped with sonar and motion sensors are introduced as a case study. Several ways to use the algorithm are suggested. Further investigations will be directed towards development of a cooperative scheme, tests in a realistic outdoor environment, and in the presence of moving obstacles.