| Project/Area Number |
14205038
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | Kyusyu Institute of Technology |
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Principal Investigator |
YAMAKAWA Takeshi Kyusyu Institute of Technology, Graduate School of Life Science and Systems Engineering, professor, 大学院生命体工学研究科, 教授 (00005547)
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| Co-Investigator(Kenkyū-buntansha) |
MIKI Tsutomu Kyusyu Institute of Technology, Graduate School of Life Science and Systems Engineering, assistant professor, 大学院生命体工学研究科, 助教授 (20231607)
ISHII Kazuo Kyusyu Institute of Technology, Graduate School of Life Science and Systems Engineering, assistant professor, 大学院生命体工学研究科, 助教授 (10291527)
HORIO Keiichi Kyusyu Institute of Technology, Graduate School of Life Science and Systems Engineering, teaching assistant, 大学院生命体工学研究科, 助手 (70363413)
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| Project Period (FY) |
2002 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥52,390,000 (Direct Cost: ¥40,300,000、Indirect Cost: ¥12,090,000)
Fiscal Year 2005: ¥9,360,000 (Direct Cost: ¥7,200,000、Indirect Cost: ¥2,160,000)
Fiscal Year 2004: ¥8,060,000 (Direct Cost: ¥6,200,000、Indirect Cost: ¥1,860,000)
Fiscal Year 2003: ¥12,870,000 (Direct Cost: ¥9,900,000、Indirect Cost: ¥2,970,000)
Fiscal Year 2002: ¥22,100,000 (Direct Cost: ¥17,000,000、Indirect Cost: ¥5,100,000)
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| Keywords | SOR networks / self-organizing maps / nonlinear manifold / real-time processing / SOM LSI / batch learning algorithm / multi-layered basis function network / adaptive control / ハードウェア / ロボティクス / 時空間パターン認識 / 自己組織化関係(SOR)ネットワーク / トレーラトラック後退制御 / 基底関数ネットワーク / 二足歩行ロボット / フローティングゲート / パルス幅変調信号 / ロボットアーム制御 / 強化学習 / 群ロボット |
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| Research Abstract |
(1)In order to improve recognition ability of mobile robot, a nonlinear manifold Self-Organizing Map (SOM) in which units include nonlinear manifolds instead of single vector in the ordinary SOM was proposed.
(2)Batch learning algorithm of the Self-Organizing Relationship (SOR) network was proposed to improve learning convergence. Furthermore, by embedding subjective evaluation criteria of users to the SOR network, the SOR network can be applied to complex system. We applied it to orbit decision of four-wheel car and control of trailer-truck buck-up control.
(3)To realize real-time processing and compactness of SOM and SOR network, SOM chip in which 25 units and 16-D reference vectors are included is implemented.
(4)It is important how to get the outer world information effectively. A multi-layered basis function network model was developed to get the outer world information and its hardware implementation was done for the real-time operation. We showed that the proposed approach was usef
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ul for human-like preprocessing such as a facial feature extraction and a character area extraction in text documents. As one of robot control methods, an effective swarm behavior generation algorithm based on simple rules also has been developed using the limited sensory information.
(5)The applications of the Self-Organizing Learning Chip into robotics are discussed through simulations and experiments. We focused on the unsupervised learning capability of the Self-Organizing Learning Chip, the algorithm of the chip is introduced into the decision making system of mobile robots. The results of obstacle avoidance simulations and experiments, and adaptive control show that the robots can take actions adaptively and adjust their decision making system on-line. And combining the decision making system and modular network SOM, the rapid adaptation to the change of dynamic property can be realized. The results show that the Self-Organizing Learning Chip is suitable to the realtime systems like robotic system. Less
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