| Project/Area Number |
15360225
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Osaka University |
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Principal Investigator |
MIYAZAKI Fumio Osaka University, Graduate School of Engineering Science, Professor, 大学院・基礎工学研究科, 教授 (20133142)
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| Co-Investigator(Kenkyū-buntansha) |
MASUTANI Yasuhiro Osaka University, Graduate School of Engineering Science, Associate Professor, 大学院・基礎工学研究科, 助教授 (80219328)
NISHIKAWA Atsushi Osaka University, Graduate School of Engineering Science, Research Associate, 大学院・基礎工学研究科, 助手 (20283731)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥8,900,000 (Direct Cost: ¥8,900,000)
Fiscal Year 2004: ¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2003: ¥7,000,000 (Direct Cost: ¥7,000,000)
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| Keywords | motion control / input-output map / LWR / ILC(iterative learning control) / Direct ILC / state estimation / motion generation / 運動学習 / メモリーベースド制御 / LWR(Locally Weighted Regression) / 学習制御 / 軌道追従 |
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| Research Abstract |
To perform tasks during intermittent interactions between robot and environment, the robot must be able to adjust the strength and timing of interactions during execution of the tasks. These are called hybrid (mixed continuous and discrete) control problems by Burridge et al., who gave hopping, catching, hitting, and juggling as typical examples. These tasks have attracted the attention of researchers also in the area of experimental psychology in the sense that they require both receptor anticipation of the environmental situation and effector anticipation of internal movement processes. However, it is difficult to find general approaches that are sufficiently tractable for the robot to perform these tasks.
In this research, we focused on the table tennis task that involves the intermittent nature of the robot-ball interaction in order to explore the hybrid control problems. We verified the usefulness of the following two methods based on the memory based learning approach to achieve a
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robotic table tennis task. (1)a method of generating stroke movement based on virtual targets that means the point at which the ball should be struck and the paddle velocity just before hitting the ball. (2)a method of controlling the paddle using three input-output maps : (a)a map for predicting the impact time of the ball hit by the paddle and the ball position and velocity at that moment according to input vectors describing the state of the incoming ball, (b)a map representing a change in ball velocities before and after the impact, and (c)a map giving the inverse relation between the ball velocity just after the impact and the bouncing point and time of the returned ball.
Contributions of this research to the robotic field are summarized as follows :
(1)to verify the effectiveness of the memory based learning approach to the hybrid control problems involved in robotic tasks with intermittent interactions between a robot and its environment,
(2)to demonstrate that a good combination of forward and inverse maps makes it possible for a robot system to interact with a dynamical environment, and
(3)to propose a Direct ILC method of perfectly realizing the robot's action planned using the maps given in (2). Less
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