Evolutionary Multi-Objective Optimization in Robot Soccer System for Education

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As the robot soccer system becomes stabilized, it has been used as an educational platform with which various topics on mobile robotics can be taught. As one of key topics in the education of mobile robotics is computational intelligence-based navigation, this paper proposes a muti-objective population-based incremental learning (MOPBIL) algorithm to obtain the fuzzy path planner for optimal path to the ball, minimizing three objectives such as elapsed time, heading direction and posture angle errors in a robot soccer system. MOPBIL employs the probabilistic mechanism, which generates new population using probability vectors. As the probability vectors are updated by referring to nondominated solutions, population converges to Pareto-optimal solution set. Simulation and experiment results show the effectiveness of the proposed MOPBIL from the viewpoint of the proximity to the Pareto-optimal set, size of the dominated space, coverage of two sets and diversity metric. By implementing each of the solutions into the educational platform, it can be educated how multi-objective optimization is realized in the real-world problem.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2009-02
Language
English
Article Type
Article
Keywords

ALGORITHMS

Citation

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, v.4, pp.31 - 41

ISSN
1556-603X
DOI
10.1109/MCI.2008.930976
URI
http://hdl.handle.net/10203/11253
Appears in Collection
EE-Journal Papers(저널논문)
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