On Behavior Evolution of Legged Robot through Knowledge Array Network
| Project/Area Number |
14550245
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | Tokai University |
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Principal Investigator |
SUZUKI Masakazu Tokai University, School of Engineering, Associate Professor, 工学部, 助教授 (40226551)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 2003: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2002: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | Robot / Behavior learning / Parameter optimization / Knowledge array / Behavior evolution / Large scale optimization |
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| Research Abstract |
The Intelligent Composite Motion Control (ICMC) is a method to realize intelligent robots that perform complex actions autonomously and adaptively. The purpose of the research is to show the effectiveness and great potential of "the robot behavior evolution through knowledge array network" based on the ICMC. The Shoot behavior in robot soccer by a quadrupedal robot TITAN-VIII was realized evolutionary based on the ICMC, and the effectiveness of the method was verified.
By repeating optimal motion compositions with adopting Joint-Rotation motions as base motions, PTP motion, Swing-Leg motion, Step motion, Approach motion and Kick motion are step-by-step learned and corresponding knowledge arrays are obtained. The knowledge array network was then constructed by hierarchically connecting the knowledge arrays. And finally Shoot behavior was realized from Approach and Kick. If the ball position is given the robot adaptively realizes the desirable shoot behavior by use of the suboptimal control parameters obtained from the knowledge array network.
On the other hand several indices for quantitative evaluation of robot behaviors such as "complexity" and "difficulty" are introduced in order to investigate effective behavior learning and evolution. The adequacy of the indices are verified by applying them to the robot soccer behavior, and relation between the index values and effective behavior learning was studied.
In addition the characteristics of the ICMC as an adaptive optimization method for large scale systems was clarified, which showed the great potential as an evolutionary design method that can applied not only to robot behavior learning and evolution but also to a wide variety of large-scale engineering system design problems.
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Report
(3 results)
Research Products
(12 results)
