Angelov, Plamen and Zhou, Xiaowei (2007) Evolving fuzzy classifier for novelty detection and landmark recognition by mobile robots. In: Mobile Roots. Studies in Computational Intelligence . UNSPECIFIED, pp. 89-118. ISBN 3540497196
Full text not available from this repository.Abstract
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.