Autonomous Robot Failure Recognition Design using Multi-Objective Genetic Programming

Author

Zhang, Yang

Author_Institution

Dept. of Electron. & Electr. Eng., Univ. of Sheffield

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

4563

Lastpage

4568

Abstract

An evolutionary autonomous failure recognition approach is presented using multi-objective genetic programming in this paper. It is compared with the conventional robot failure classification algorithm. Detailed analysis of the evolved feature extractors is tempted on investigated problems. We conclude MOGP is an effective and practical way to automate the process of failure recognition system design with better recognition accuracy and more flexibility via optimizing feature extraction stage

Keywords

control system synthesis; feature extraction; genetic algorithms; mobile robots; evolutionary autonomous robot failure recognition design; feature extraction; multiobjective genetic programming; robot failure classification algorithm; Artificial intelligence; Decision making; Design optimization; Feature extraction; Filter bank; Genetic programming; Machine learning; Mobile robots; Robot programming; Robot sensing systems; Robotics and automation; Sensor systems; Service robots; Autonomous robot; Failure recognition; Feature Extraction; Multi-objective Genetic Programming;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258378

Filename

4028876