Autonomous Mobile Robot with Visual Neural Network
This paper describes a new approach to control systems for an autonomous mobile robot by using sandwiches of two different kinds of neural networks. One is a neural network for recognizing sensor information with a mechanism of competition and cooperation, where synaptic couplings are fixed. The second is a neural network with adaptive synaptic couplings corresponding genotype in the creature and used for self- learning of wheel controls. In the computer simulative model with both two parts of neural network, we are successful to obtain typical types of robot with a good performance when going along the carved wall. The first part of the networks play a role to make a decision among sensor signals under noisy environment, while the second part is effective to adjust the synaptic couplings through genetic operations so that it may transfer the outputs from the first stage with the competitioncooperation neural network (CCNN) to the rotation of robots wheel. A test is performed to show the superiority of CCNN. A robot with CCNN can enter into a narrow entrance with concaved space and strong robustness against several kinds of noises, while the robot without CCNN cannot enter into the space due to the surrounding noise around the entrance.
TETSURO KITAZOE MASAYOSHI TABUSE TATSURO SHINCHI AKINOBU TODAKA YUKA TOKUSHIGE
Dept. of Computer Science and Systems Engineering,Faculty of Engineering, Miyazaki University Gakuen Kibanadai Nishi 1-1, Miyazaki-city, Japan, 889-2192
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1171-1176
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）