Online Multiobjective Evolutionary Approach for Navigation of Humanoid Robots

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This paper proposes a novel online multiobjective evolutionary approach for the navigation of humanoid robots. In the proposed approach, the humanoid robot navigation problem is decomposed into a series of small multiobjective optimization problems (MOPs) with corresponding local information. Using multiobjective evolutionary algorithms (MOEAs), the MOPs can be successively solved while the robot is walking. In addition, to achieve significant reductions in the processing time of the MOEAs for online implementation while maintaining robustness and scalability, a novel homogeneous parallel computing method is devised for the MOEAs. Multiobjective particle swarm optimization with preference-based sort (MOPSO-PS) is employed as the MOEA to reflect the user-defined preference for each objective during navigation. The effectiveness of the proposed online approach is demonstrated through well-known benchmark problems and a robot simulator. In both the simulation and the experiment, a humanoid robot successfully navigates to the goal, satisfying the preferences for various objectives, with local information in an environment without a global map.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-02
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, v.62, no.9, pp.5586 - 5597

ISSN
0278-0046
DOI
10.1109/TIE.2015.2405901
URI
http://hdl.handle.net/10203/204044
Appears in Collection
EE-Journal Papers(저널논문)
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