JACIII Vol.15 No.5 pp. 525-531
doi: 10.20965/jaciii.2011.p0525


Learning of Obstacle Avoidance with Redundant Manipulator by Hierarchical SOM

Yuichi Kobayashi and Takahiro Nomura

Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

January 31, 2011
April 16, 2011
July 20, 2011
redundant manipulator, collision avoidance, self-organizing map, reinforcement learning

This paper proposes a method of obstacle avoidance motion generation for a redundant manipulator with a Self-OrganizingMap (SOM) and reinforcement learning. To consider redundancy, two types of SOMs – a hand position map and a joint angle map – are combined. Multiple joint angles corresponding to the same hand position are memorized in the proposed map. Preserved redundant configuration information is used to generate motions based on tasks and situations, while resolving inverse kinematics problems with a redundant manipulator. The proposed map is applied to planning motion control using reinforcement learning in an unknown environment, where collision with obstacles is detected only directly by tactile sensing. The feasibility of the proposed framework was verified by simulation and experiments with an arm robot with force and a vision sensors.

Cite this article as:
Yuichi Kobayashi and Takahiro Nomura, “Learning of Obstacle Avoidance with Redundant Manipulator by Hierarchical SOM,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.5, pp. 525-531, 2011.
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