| Project/Area Number |
07245105
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Research Institution | Osaka University |
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Principal Investigator |
SHIRAI Yoshiaki Osaka University, Faculty of Engineering, Professor, 工学部, 教授 (50206273)
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| Co-Investigator(Kenkyū-buntansha) |
KAKIKURA Masayoshi Tokyodenki University, Faculty of Engineering, Professor, 工学部, 教授 (90224344)
HASEGAWA Tsutomu Kyusyu University, Faculty of Engineering, Professor, 工学部, 教授 (00243890)
MORI Hideo Yamanashi University, Faculty of Engineering, Professor, 工学部, 教授 (40020383)
INOUE Hiromitsu The University of Tokyo, Faculty of Engineering, Professor, 大学院・工学系研究科, 教授 (50111464)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥104,700,000 (Direct Cost: ¥104,700,000)
Fiscal Year 1997: ¥28,800,000 (Direct Cost: ¥28,800,000)
Fiscal Year 1996: ¥34,100,000 (Direct Cost: ¥34,100,000)
Fiscal Year 1995: ¥41,800,000 (Direct Cost: ¥41,800,000)
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| Keywords | Environment Recognition / Mobile Robot / Work Planning / Optimal Planning / Realtime Vision / Sensor Fusion / Learning / センサーフュージョン |
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| Research Abstract |
Tightly coupled sensor-behavior is important for intelligent systems to behave in the real world. Such systems should deal with uncertainty of sensor information, lack of information of the environment, uncertainty of motion, and uncertainty of a priori knowledge. Tightly coupled sensor-behavior is a promising approach to this problem. The result of this research is the followings :
・ Interpretation of Sensor Information for Real World Understanding : We studied fusion of various visual information to recognize environments, and implemented the methods in J.Rob.Vision (developed in this research) to realize realtime processing. Considering the reliability of recognition results, we made the optimal plan generation method which minimize the cost of sensing and that of motions. Finally, we made experiments of understanding real environments by sensing and actions.
・ Sensing mechanism for Behaviors Adaptive to the Real World : In order to move in a changing environment such as roads, we int
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egrated sonar-based obstacle detection, sign pattern-based recognition of roads and cars, and sign pattern learning during motion of a robot, and then we developed a guide robot for a blind people.
・ Behavior Control Based on Environment Model and Sensory Information : By developing a three-finger manipulation system, we realized realtime detection of object posture by stereo vision and detection of grasping force on fingers. We proposed error avoidance by monitoring task states and verified by assembly experiments. Moreover, we realized a system which selects a suitable cloth in a pile of clothes by a recursive region split method, and picks it up by a hand with a special rotor.
・ Adaptive Functions in Perception-Action Systems : We proposes "behavior network" which represents and controls concurrent process of selecting sensor-behavior and of predicting the environment based on the memory, and studied how to represent adaptive behaviors of a robot. We made a four legged robot with vision on J.Rob.Leg(developed in this research). By experiments of the adaptive behaviors in complex environments, we verified the performance of the behavior network. Less
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