Summary
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
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101002724 |
| Start date: | 01-08-2021 |
| End date: | 31-07-2026 |
| Total budget - Public funding: | 1 973 485,00 Euro - 1 973 485,00 Euro |
Cordis data
Original description
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
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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