| Project/Area Number |
21K14191
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 21040:Control and system engineering-related
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| Research Institution | National Institute of Advanced Industrial Science and Technology (2022-2023)
National Institute of Informatics (2021) |
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Principal Investigator |
Pruekprasert Sasinee 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (50814795)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | symbolic control / control theory / motion planning / robotics / temporal logic |
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| Outline of Research at the Start |
This project aims to develop an efficient symbolic control framework for temporal logic specifications in semi-controlled environments. Symbolic control is a well-known approach to synthesizing a provably correct controller under complex specifications such as temporal logic, but this approach's main limitation is its scalability. We will develop a fast symbolic controller synthesis algorithm by performing samplings as guides for the symbolic abstraction. We will also study motion planning for nonholonomic robots based on the proposed framework.
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| Outline of Final Research Achievements |
This research project studied safe control frameworks for semi-controlled environments in which some disturbance may occur. The overall goal of the project is to develop safe and efficient symbolic-structure-based control and verification techniques that are robust enough to handle systems under some nondeterminism and uncertainties. Throughout the research period, we developed efficient symbolic control algorithms for safety verification and control under temporal logic specficiations, and demonstrated their performances by simulations on nonholonomic robots. We studied a safe learning approach utilizing discrete symbolic structures. We also developed a moment approximation method of a stochastic polynomial system, which can be used for system safety verification.
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| Academic Significance and Societal Importance of the Research Achievements |
Our research project provides safe system verification and control approaches that are suitable for managing safety-critical systems.
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