2005 Fiscal Year Final Research Report Summary
Development of a cognitive map for mobile robot and its advancement inspired by place cells in hippocampus
| Project/Area Number |
15500140
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
ISHIKAWA Masumi Kyushu Institute of Technology, Graduate School of Life Science and Systems Engineering, Professor, 大学院生命体工学研究科, 教授 (60222973)
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| Co-Investigator(Kenkyū-buntansha) |
SHO Hiroshi Kyushu Institute of Technology, Graduate School of Life Science and Systems Engineering, Assistant Professor, 大学院生命体工学研究科, 助手 (30235709)
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| Project Period (FY) |
2003 – 2005
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| Keywords | mobile robot / module / self-organizing map / concept formation / segmentation / navigation / grid-based map / graph-based map |
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| Research Abstract |
1. Appropriately setting an angle of elevation of an object, we calculate the angle of inclination of curved surface. Providing the resulting angle as an input to a self-organizing map, we train a one-dimensional SOM. Iterative computation provides a design of an omni-directional mirror with resolution proportional to probability density function of an angle of elevation. It also turned out that, in some probability density functions, realization of the resolution proportional to probability density function is not possible using a convex mirror, but is possible using a convex and concave mirror.
2. Taking advantage of the fact that translation invariance of local higher-order autocorrelation functions for panoramic images is equivalent to rotation invariance of polar local higher-order autocorrelation functions(PHLAC) for omni-directional images, we succeeded in obtaining rotation invariant features. Combination of these 35-dimensional PHLAC features and particle filters realized real-time localization (position and orientation) of a mobile robot, Khepera II, with average position error of 31mm and orientation error of 5.5 degrees.
3. We succeeded in segmentation using modular network self-organizing maps (mnSOM), and in emerging three abstract concepts, i.e., straight movement, right turn and left turn, based on sensory data. Application of novel data to the resulting mnSOM realized the correct segmentation rate of 95.2%. Based on the resulting mnSOM, we also succeeded in constructing a graph-based map from a grid-based map for a simple environment.
4. In integrating sensory information at different locations, data mismatch occurs when an obstacle gradually moves. We found that combination of forward sensory models and an EM algorithm can resolve the data mismatch.
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