| Project/Area Number |
16300050
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
ARAI Tamio Univ. of Tokyo, Dept. of Precision Engineering, Professor, 大学院・工学系研究科, 教授 (40111463)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMOMURA Yoshiki Tokyo Metropolitan Univ., Dept. of System Design, Professor, システムデザイン学部, 教授 (80334332)
OTA Jun Univ. of Tokyo, Graduate School of Engineering, Associate Prof., 大学院・工学系研究科, 助教授 (50233127)
MAEDA Yusuke Yokohama National University, Dept. of Systems Design, Associate Prof., 大学院・工学研究院, 助教授 (50313036)
SUGI Masao Univ. of Tokyo, Graduate School of information Science and Technology, Project Research Associate, 大学院・情報理工学系研究科, 研究拠点形成特任助手 (90372408)
UEDA Ryuichi Univ. of Tokyo, Graduate School of Engineering, Research Associate, 大学院・工学系研究科, 助手 (20376502)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥11,600,000 (Direct Cost: ¥11,600,000)
Fiscal Year 2006: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2005: ¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2004: ¥6,100,000 (Direct Cost: ¥6,100,000)
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| Keywords | state-action map / compression of state-action map / RoboCup / dynamic programming / RoboCup / 戦略的行動と学習 / state-action map / mobile robot in real world / simulator for sensors / vector quantization / Monte Carlo localization / resetting methods |
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| Research Abstract |
In this research, we have developed a novel set of methodology that makes autonomous robots act in the real-world for long time. For technology exchange, also, we have participated in the robot soccer world cup (RoboCup), where researchers have make robots work in the real-world successfully.
The methods developed are as follows: self-localization that is robust against unexpected conditions, a decision making method that cover any condition in a task, and another decision making method that deals with uncertainty of recognition.
Our novel self-localization method, which is a version of a particle filter, utilizes resetting methods. A resetting method is used when a particle filter loses the pose of a robot. We enhance the robustness of a particle filter for self-localization by combining two major resetting methods, which have antipodal characteristics.
As a planning and decision making method, we have proposed a compression method for decision making data, which is huge for install on memory of robots. Vector quantization is used for the compression.
The particle filter and the decision making rule is combined so that robots can deal with the problem of uncertainty in decision making. In this method, an expected value of appropriateness is calculated for each action toward the probability distribution represented by the particle filter. The most appropriate action is then chosen from the values. In experiments, robots can switch their behavior when the level of uncertainty is changed. For example, a robot keeps away from a wall of an environment when the distance between the robot and the wall is uncertain.
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