| Project/Area Number |
11680393
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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 informatics
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
ISHIKAWA Masumi Kyushu Institute of Technology Dept. of Brain Science & Engineering, Professor, 大学院・生命体工学研究科, 教授 (60222973)
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| Co-Investigator(Kenkyū-buntansha) |
HONG Zhang Kyushu Institute of Technology Dept. of Brain Science & Engineering, Assistant Professor, 大学院・生命体工学研究科, 助手 (30235709)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2001: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2000: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1999: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Keywords | robots / navigation / self-organization / omni-directional mirror / environmental map / scene images |
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| Research Abstract |
1) Local autocorrelation functions proposed by otsu is appropriate for compressing scene images. We clarified that special resolution of 1/64 is the best based on the criterion of minimizing location estimation error.
2) In constructing environmental maps by self-organization, inputs to SOM are made by adding location information to local autocorrelation functions of each scene image. Location information of a robot is corrupted by noise. In contrast to conventional Bayesian estimation, prior probability distribution is assumed to be Gaussian. A relative weight of location information is determined by minimizing location estimation error.
3) Real robot is examined in a field including obstacles made of corrugated cardboard. As the robot proceeds, location and direction errors increase due to slipping of wheels, which is experimentally measured. As mentioned above, location information including error is added to local autocorrelation functions and is used as an extended input to SOM.
4) When a new input vector is given, Bayesian estimation is effective in estimating location using constructed environmental maps. In contrast to conventional Bayesian estimation, prior probability at some time is a translation of a posterior probability at previous time due to movement of a robot.
5) Since an environmental map does not include the location of obstacles, it is difficult to combine location estimation and obstacle avoidance. We propose to represent the distance between an infrared sensor and the an obstacle in the form of probability distribution, and demonstrate its effectiveness by simulation experiments.
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