DocumentCode :
396770
Title :
Reinforcement learning for hierarchical and modular neural network in autonomous robot navigation
Author :
Calvo, Rodrigo ; Figueiredo, Mauricio
Author_Institution :
Dept. of Comput. Sci., Maringa State Univ., Brazil
Volume :
2
fYear :
2003
fDate :
20-24 July 2003
Firstpage :
1340
Abstract :
This work describes an autonomous navigation system based on a modular neural network. The environment is unknown and initially the system does not have ability to balance two innate behaviors: target seeking and obstacle avoidance. As the robot experiences some collisions, the system improves its navigation strategy and efficiently guides the robot to targets. A reinforcement learning mechanism adjusts parameters of the neural networks at target capture and collision moments. Simulation experiments show performance comparisons. Only the proposed system reaches targets if the environment presents a high risk (dangerous) configuration (targets are very close to obstacles).
Keywords :
collision avoidance; hierarchical systems; learning (artificial intelligence); mobile robots; navigation; neural nets; target tracking; autonomous navigation system; hierarchical network; mobile robot; modular neural network; obstacle avoidance; reinforcement learning; target seeking; Computer science; Control systems; Intelligent networks; Intelligent systems; Learning; Motion planning; Navigation; Neural networks; Robot kinematics; Robot sensing systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1223890
Filename :
1223890
Link To Document :
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