Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
}
TY - CHAP
T1 - Evolving Fuzzy Classifier for Real-time Novelty Detection and Landmark Recognition by a Mobile Robot
AU - Angelov, Plamen
AU - Zhou, Xiaowei
PY - 2007
Y1 - 2007
N2 - In this chapter, an approach to real-time landmark recognition and simultaneous classi¯er design for mobile robotics is introduced. The approach is based on the recently developed evolving fuzzy systems (EFS) method [1], which is based on sub- tractive clustering method [2] and its on-line evolving extension called eClustering [1]. When the robot travels in an unknown environment, the landmarks are auto- matically deteced and labelled by the EFS-based self-organizing classi¯er (eClass) in real-time. It makes fully autonomous and unsupervised joint landmark detec- tion and recognition without using the absolute coordinates (altitude or longitude), without a communication link or any pre-training. The proposed algorithm is re- cursive, non-iterative, incremental and thus computationally light and suitable for real-time applications. Experiments carried out in an indoor environment (an o±ce located at InfoLab21, Lancaster University, Lancaster, UK) using Pioneer3 DX mo- bile 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 the presence of moving obstacles. (c) Springer
AB - In this chapter, an approach to real-time landmark recognition and simultaneous classi¯er design for mobile robotics is introduced. The approach is based on the recently developed evolving fuzzy systems (EFS) method [1], which is based on sub- tractive clustering method [2] and its on-line evolving extension called eClustering [1]. When the robot travels in an unknown environment, the landmarks are auto- matically deteced and labelled by the EFS-based self-organizing classi¯er (eClass) in real-time. It makes fully autonomous and unsupervised joint landmark detec- tion and recognition without using the absolute coordinates (altitude or longitude), without a communication link or any pre-training. The proposed algorithm is re- cursive, non-iterative, incremental and thus computationally light and suitable for real-time applications. Experiments carried out in an indoor environment (an o±ce located at InfoLab21, Lancaster University, Lancaster, UK) using Pioneer3 DX mo- bile 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 the presence of moving obstacles. (c) Springer
KW - DCS-publications-id
KW - incoll-63
KW - DCS-publications-credits
KW - dsp-fa
KW - DCS-publications-personnel-id
KW - 82
KW - 102
M3 - Chapter
SN - 978-3-540-49719-6
VL - 50
T3 - Studies in Computational Intelligence
SP - 95
EP - 124
BT - Mobile Robots: The Evolutionary Approach
A2 - Nedja, N
A2 - Coelho, L
A2 - Mourelle, L
PB - Springer
CY - Berlin/Heidelberg
ER -