教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Author: search.filters.author.Nai-Jen Li

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    Global Optimization Using Novel Randomly Adapting Particle Swarm Optimization Approach
    (2011-10-12) Nai-Jen Li; Wen-June Wang; Chen-Chien Hsu; Chih-Min Lin
    This paper proposes a novel randomly adapting particle swarm optimization (RAPSO) approach which uses a weighed particle in a swarm to solve multi-dimensional optimization problems. In the proposed method, the strategy of the RAPSO acquires the benefit from a weighed particle to achieve optimal position in explorative and exploitative search. The weighed particle provides a better direction of search and avoids trapping in local solution during the optimization process. The simulation results show the effectiveness of the RAPSO, which outperforms the traditional PSO method, cooperative random learning particle swarm optimization (CRPSO), genetic algorithm (GA) and differential evolution (DE) on the 6 benchmark functions.
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    Localization of Mobile Robots via an Enhanced Particle Filter
    (2010-05-06) Chen-Chien Hsu; Ching-Chang Wong; Hung-Chih Teng; Nai-Jen Li; Cheng-Yao Ho
    A self-localization method entitled enhanced particle filter incorporating tournament selection and Nelder-Mead simplex search (NM-EPF) for autonomous mobile robots is proposed in this paper. To evaluate the performance of the localization scheme, an omnidirectional vision device is mounted on top of the robot to analyze the environment of a soccer robot game field. Through detecting the white boundary lines relative to the robot in the game field, weighting for each particle representing the robot's pose can be updated via the proposed NM-EPF algorithm. Because of the efficiency of the NM-EPF, particles converge to the correct location of the robot in a responsive way while tackling uncertainties. Simulation results have shown that efficiency in robot self-localization can be significantly improved while maintaining a relatively smaller mean error in comparison to that via conventional particle filter.
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