2006 Fiscal Year Final Research Report Summary
Semi-autonomous Risk Assessment System with Incidents Reporting Function
| Project/Area Number |
17300067
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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 | National Institute of Advanced Industrial Science and Technolog |
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Principal Investigator |
YAMADA Yoji National Institute of Advanced Industrial Science and Technolog, Intelligent Systems Research Institute, Group Leader, 知能システム研究部門, 研究グループ長 (90166744)
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| Co-Investigator(Kenkyū-buntansha) |
PGIRE Takuya National Institute of Advanced Industrial Science and Technolog, Intelligent Systems Research Institute, Researcher, 知能システム研究部門, 研究員 (60415651)
YOON Seong-Sik National Institute of Advanced Industrial Science and Technolog, Intelligent Systems Research Institute, Post doctoral researcher, 知能システム研究部門, ポストドクトラル研究員 (10443235)
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| Project Period (FY) |
2005 – 2006
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| Keywords | human error / hazard / Hidden Markov Model / predictor / incident report / multi-modal information / M-SHELL model / Skill-Assist |
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| Research Abstract |
The study aimed at automating a series of risk assessment process from risk perception to supplying alarming information to a worker maneuvering Skill-Assist in order not to encounter any hazardous situation. Discussion on the study proceeds based on the hypothesis that hazard point information is known at hand.
We developed various component techniques toward the semi-autonomous risk assessment process. First, we built a human body part motion trajectory detector by use of an accelerometer and a gyroscope promising high robustness against environmental noise. We also developed a predictor which generated inputs in several seconds ahead through acquisition of the sensory information of the current position and the maneuvering force that human exerted. They were incorporated into the dynamics of Skill-Assist and the state equation for the impedance control. Regarding the text-mining technology, we implemented a CODING program which encodes word groups into its class codes for enhancing t
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he performance of text-clustering or information-retrieval operation. A sensor-aided Multimodal Incident Report System (MIRS) was also developed, which enabled persons to intuitively report dangerous situations. All records from sensors contribute to expressing incidents and their causal factors in a highly systematized data structure based on the m-SHEL model.
Finally, we conducted experiments investigating the convergence of the developed Skill-Assist system so as not to cause any accident/incident after enhancing the safety level of the system : duplicating all sensor signal channels as well as the controllers heterogeneously for self-diagnosing purposes. We experimentally adjusted the prediction time, which was equivalent to expansion/contraction of the volume in the vicinity of hazard points, by the verbal information from the subject. It is eventually concluded that the results of the experiment under the vicinity area of 22 mm radius showed prediction time did not converge up to 0 s of adjustment time. It is demanded that further experiments be conducted with smaller volume around the hazard points. Less
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