Title :
Autonomous Robot Failure Recognition Design using Multi-Objective Genetic Programming
Author_Institution :
Dept. of Electron. & Electr. Eng., Univ. of Sheffield
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;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258378
