RIDING
Financiado
Physics informed algorithms for sensing and navigating turbulent environments
Living systems developed dramatically efficient strategies to sense and navigate turbulent environments. Understanding these strategies is key to many real world applications required to function in the presence of turbulence: fro...
Living systems developed dramatically efficient strategies to sense and navigate turbulent environments. Understanding these strategies is key to many real world applications required to function in the presence of turbulence: from search and rescue to demining and patrolling. While much is known on navigation in smooth environments, these approaches fail in the presence of turbulence. RIDING aims at elucidating the computations organisms use to extract useful information from turbulent stimuli and navigate to a target. A key observation is that organisms rely on multiple sensory cues, despite the distortions due to turbulence. Explaining this puzzle requires blending fluid dynamics with biological behavior. I will achieve this goal by developing physics-based algorithms elucidating the computations that support three fundamental pillars of biological behavior: 1) combine navigation with sensing, 2) balance multiple senses, 3) adapt to different environments. The result will be a comprehensive theory integrating biological behavior in a computational framework based on fluid dynamics. Predictions will be tested via experiments on fishes, known to routinely perform turbulent navigation combining multiple senses across distinct sensory environments. This multidisciplinary project leverages methods from physics, computer science and biology. In summary, the objectives of RIDING are to:
O1. Assemble a massive dataset of chemical and mechanical signals emitted by a target using computational fluid mechanics and asymptotic methods.
O2. Develop algorithmic approaches for sensing and navigation using tools from machine learning trained on multiple sensory signals from O1.
O3. Examine how sensory signals from O1 and algorithms from O2 vary in different environments.
O4. Test predictions by recording prey capture in the laboratory using three species of fish. Analysis includes a fascinating species which evolved unique sensory legs to catch prey in different environment
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Duración del proyecto: 60 meses
Fecha Inicio: 2021-07-15
Fecha Fin: 2026-07-31
Fecha Fin: 2026-07-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2021-07-15
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-COG:
ERC CONSOLIDATOR GRANTS
Cerrada
hace 4 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
UNIVERSITA DEGLI STUDI DI GENOVA
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
603.62K€ |
Participante