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Evolutionary Fuzzy Control and Navigation of Multiple Cooperative Robots
|關鍵字:||多台合作型機器導航;Navigation of Multiple Cooperative Robots;進化模糊控制;Evolutionary Fuzzy Control||出版社:||電機工程學系所||引用:||Reference  S. Nolfi and D. Floreano, Evolutionary Robotics – The Boloogy, Intelligence, and Technology of Self-Organizing Machines, MIT Press, London, England, 2001.  W. Jatmiko, K. Sekiyama, and T. Fukuda, “A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement,” IEEE Computational Intelligence Magazine, vol. 2, no. 2, pp. 37-51, 2007.  C. F. Juang and Y. C. Chang, “Evolutionary group-based particle swarm-optimized fuzzy controller with application to mobile robot navigation in unknown environments,” IEEE Trans. Fuzzy Systems, vol. 19, no. 2, pp. 379-392, April 2011.  D. W. Gong, Y. Zhang, and C. L. Qi, “ Localising odour source using multi-robot and anemotaxis-based particle swarm optimization,” IET Control Theory & Applications, vol. 6, no. 11, pp. 1661-1670, 2012.  C. F. 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This paper proposes navigation of multiple wheeled mobile robots cooperatively carrying an object in unknown environments. In the navigation process, multiple robots cooperatively perform either an obstacle-boundary-following (OBF) or a target seeking (TS) behavior to reach a target. Evolutionary fuzzy control of two/three robots in executing the cooperative OBF behavior through adaptive fusion of continuous ant colony and particle swarm optimization algorithms (AF-CACPSO) is proposed. All of the free parameters in a fuzzy controller (FC) are learned through the AF-CACPSO, which avoids the time-consuming manual design task. The AF-CACPSO-designed FC is first applied to the control of a single robot for the OBF behavior in a training environment. The learning approach is then applied to address the cooperative OBF problem of two/three cooperative robots, where auxiliary FCs for the other robots are designed using the AF-CACPSO. For the cooperative TS behavior, a rule for coordination of the two/three robots is proposed. In navigation, a cooperative behavior supervisor is proposed to coordinate the learned cooperative OBF behavior and the cooperative TS behavior, where the problem of dead cycles is considered. Performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms (POAs) in the cooperative OBF behavior learning problem. Successful navigation of two/three cooperative robots in simulations and experiments verify effectiveness of the proposed evolutionary fuzzy control and navigation approaches.
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