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Información general |
| Autor |
Yang, Jingan; Li, Chunguang; Zhuang, Yanbin |
| Publicado |
InTech Open Access Publisher, 2013
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| Abstract |
This paper proposes a behavior‐switching
control strategy of an evolutionary robot based on
Artificial Neural Network (ANN) and genetic algorithm
(GA). This method is able not only to construct the
reinforcement learning models for autonomous robots
and evolutionary robot modules that control behaviors
and reinforcement learning environments, and but also to
perform the behavior‐switching control and obstacle
avoidance of an evolutionary robot in the unpredictable
environments with the static and moving obstacles by
combining ANN and GA.
The experimental results have demonstrated that our
method can perform the decision‐making strategy and
parameter setup optimization of ANN and GA by
learning and can effectively escape from a trap of local
minima, avoid motion deadlock status of humanoid
soccer robotic agents, and reduce the oscillation of the
planned trajectory among the multiple obstacles by
crossover and mutation. We have successfully applied
some results of the proposed algorithm to our simulation
humanoid robotic soccer team CIT3D which won the 1st
prize of RoboCup Championship and ChinaOpen2010
and the 2nd place of the official RoboCup World
Championship on 5‐11, July 2011 in Istanbul, Turkey.
In comparison with the conventional behavior network
and the adaptive behavior method, our algorithm
simplified the genetic encoding complexity, improved the
convergence rate and the network performance. |
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International Journal of Advanced Robotic Systems
Autor: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publicado: 2004
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