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Author:

Pang, Tao (Pang, Tao.) | Ruan, Xiaogang (Ruan, Xiaogang.) | Wang, Ershen (Wang, Ershen.) | Fan, Ruiyuan (Fan, Ruiyuan.)

Indexed by:

EI

Abstract:

For the search and rescue robot navigation in unknown environment, a bionic self-learning algorithm based on A* and Q-Learning is put forward. The algorithm utilizes the Growing Self-organizing Map (GSOM) to build the environment topology cognitive map. The heuristic search A* algorithm is used the global path planning. When the local environment changes, Q-Learning is used the local path planning. Thereby the robot can obtain the self-learning skill by studying and training like human or animal, and looks for a free path from the initial state to the target state in unknown environment. The theory proves the validity of the method. The simulation result shows the robot obtains the navigation capability. © 2012 Universitas Ahmad Dahlan.

Keyword:

Self organizing maps Conformal mapping Learning algorithms Navigation Reinforcement learning Heuristic algorithms Educational robots Robots Motion planning Robot programming Cognitive systems

Author Community:

  • [ 1 ] [Pang, Tao]Institute of Artificial Intelligence and Robots, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Pang, Tao]College of Electronics and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
  • [ 3 ] [Ruan, Xiaogang]Institute of Artificial Intelligence and Robots, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Wang, Ershen]College of Electronics and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
  • [ 5 ] [Fan, Ruiyuan]Institute of Artificial Intelligence and Robots, Beijing University of Technology, Beijing 100124, China

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Source :

Telkomnika - Indonesian Journal of Electrical Engineering

ISSN: 2302-4046

Year: 2012

Issue: 7

Volume: 10

Page: 1889-1896

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 13

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