| Project/Area Number |
19F19722
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | Ochanomizu University |
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Principal Investigator |
オベル加藤 ナタナエル お茶の水女子大学, 基幹研究院, 助教 (10749659)
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| Co-Investigator(Kenkyū-buntansha) |
CAZENILLE LEO お茶の水女子大学, 基幹研究院, 外国人特別研究員
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| Project Period (FY) |
2019-04-25 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2020: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2019: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Molecular robotics / Swarm robotics / Molecular programming / Evolutionary robotics / Quality-diversity / MAP-Elites / Biochemical-micro-robots / Surrogate models / Reaction-diffusion |
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| Outline of Research at the Start |
We use evolutionary optimization methods and quality-diversity algorithms to design controllers for a swarm of bio-micro-robots. The robots are made of simple beads coated with DNA, which implements both the controller through reaction-diffusion and an anchoring scheme, allowing self-assembly.
Designing appropriate DNA-based chemical reaction networks to serve as robotic controllers is a challenging problem involving non-linear dynamics and high experimental variability. The algorithms selected for this project will allow us to explore the dynamics and trade-offs available to this approach.
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| Outline of Annual Research Achievements |
Our aim was to design controllers for self-organizing swarms of micro-robots with specific collective dynamics emerging from local interactions. In particular, we focused on large robotic swarms with millions micrometer-sized micro-robots composed of sepharose beads controlled through the reaction-diffusion dynamics of DNA strands grafted into the robots. We extended the methodology implemented the previous year to automatically design these DNA-based controllers, by using optimization and quality-diversity algorithms and testing promising solutions in simulations. However this approach was especially computationally expensive, in particular because of the complex reaction-diffusion simulations involved. As such, we presented a new method to improve the computational efficiency of such simulations using GPUs with the CUDA framework (paper "Accelerating the Finite-Element Method for Reaction-Diffusion Simulations on GPUs with CUDA"). Another limitation of our methodology to automatically design robot controllers was that the user previously had to manually provide scores ("feature descriptors") quantifying how diverse the tested solutions were, a typical aspect of quality-diversity algorithms. We described a new methodology to find automatically these diversity scores for a target problem (paper "Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms").
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| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
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