A study of interaction scheme and external computation resource for heterogenous autonomous robots
| Project/Area Number |
17500117
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | Future University-Hakodate |
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Principal Investigator |
OSAWA Eiichi Future University-Hakodate, School of System Information Science, Professor, システム情報科学部, 教授 (60325884)
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| Co-Investigator(Kenkyū-buntansha) |
SUZUKI Keiji Future University-Hakodate, School of System Information Science, Professor, システム情報科学部, 教授 (10250482)
MIKAMI Sadayoshi Future University-Hakodate, School of System Information Science, Professor, システム情報科学部, 教授 (50229655)
KATO Koji Future University-Hakodate, School of System Information Science, Associate Professor, システム情報科学部, 准教授 (30273874)
OKUNO Taku Future University-Hakodate, School of System Information Science, Associate Professor, システム情報科学部, 准教授 (30360936)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2006: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2005: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | Distributed system / Autonomous Roboto / Mixture Scheme / Reinforcement learning / Integrated architecture / Collision avoidance / Agent platform / エージェントプラットフォーム / 自律ロボット / 分散協調システム / アドホックネットワーク / マルチエージェントシステム / ロボット間通信 / モバイルエージェント |
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| Research Abstract |
We have developed an integrated architecture for multiple small mowing robots. The architecture includes (1) a mixture scheme to improve reliability of robots, (2) agent framework to enable cooperation among robots, (3) a scheduling methods for robots, and (4) learning scheme for robot action policy. The developed mowing robots consists of a basic mowing unit, CPU, GPS, and communication module as well.
The previous work shows that small mowing robots suffers from several problems. They include (1) robots easily stack on uneven surface, (2) low path trace capability, and (3) communication is unstable in the outside environment Furthermore, robots need to be recharged when they gets lower electricity energy. Tb handle these problems, we need to device robust and efficient working strategy for robots, which includes robots and charging stations. For that purpose, we have developed mixture scheme to improve the reliability of robots.
Also, we have developed agent framework based on Web service technology, which enables cooperation among robots. The framework includes Java mobile agent Robots are able to cooperate based on the framework We also show efficiency of the scheme.
As far as the learning of action policy of robots is concerned, we have developed two learning features of robots. One is for collision avoidance, and the other is for on demand learning of escape from stuck situation. The simulation results shows efficiency of these features. We also implemented these feature on the real robots. The experimental results of the real robots are consistent with the simulation results.
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Report
(3 results)
Research Products
(19 results)
