Paper Info
Title
Recurrent Neural Network With Self-Adaptive Gas For Biped Locomotion Robot
Abstract
In this paper, we propose a generation method of a stable motion of a biped locomotion robot. We apply the proposed method to eight force sensors at the soles of the biped locomotion robot. Zero Moment Point (ZMP) is a well known as the index of stability in walking robots. ZMP is determined by the configuration of the robots. However, there are many configurations against the ZMP. Because of that, when we use ZMP as stabilization index, we must select the best among many stability configurations. Then it is a problem that which configuration is selected. In this paper, The problem can be solved with recurrent neural network. We calculate the position of ZMP and the joints and the angles that should be actuated can be determined by recurrent neural network without ZMP moving out from. the supporting area of sole. We employ the recurrent neural network with self-adaptive GAs for learning capability. Further, we build a biped locomotion robot in trial, which has 13 joints and verified that the calculated stability motion trajectory can be successfully applied to the practical biped locomotion, In this paper, we propose a way of training of reccurrent neural network for biped locomotion robot.
Year
DOI
Venue
1997
10.1109/ICNN.1997.614153
1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4
Keywords
Field
DocType
neural networks,spirals,genetics,actuators,genetic algorithms,neural network,gears,path planning,robots,force sensor,recurrent neural network,recurrent neural networks,foot,zero moment point,motion control,mobile robots,learning artificial intelligence
Motion planning,Motion control,Control theory,Computer science,Recurrent neural network,Zero moment point,Robot,Trajectory,Mobile robot,Genetic algorithm
Conference
Citations 
PageRank 
References 
4
1.17
0
Authors
3
Name
Order
Citations
PageRank
Toshio Fukuda12723818.58
Y. Komata241.17
Tetsuo Arakawa3466.93