Evolving fuzzy classifier for novelty detection and landmark recognition by mobile robots

Angelov, Plamen and Zhou, Xiaowei (2007) Evolving fuzzy classifier for novelty detection and landmark recognition by mobile robots. In: Mobile Roots : The Evolutionary Approach. Studies in Computational Intelligence . UNSPECIFIED, pp. 89-118. ISBN 3540497196

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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.

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24 Mar 2020 14:25
Last Modified:
16 Jul 2024 04:53