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Biological organisms exhibit remarkable proficiency in locomotion. From sea stars walking on rocky and uneven terrains to microscopic organisms swimming through viscous fluids, they are well-adapted to their environments. Effective motion in such environments requires the ability to sense local cues, infer information about their surroundings, and use that information for effective navigation and control. This challenge becomes particularly pronounced in fluid environments, such as vortical flows, where swimmers must adapt dynamically to unpredictable conditions, or in Stokes flow where viscous forces are dominant and inhibit swimming.

 

In this talk, I will explore how recent advances in artificial intelligence (AI) can be applied to the design of bio-inspired swimmers operating in various environments. I will specifically focus on three model problems: a three-link fish in potential flow, a pair of schooling swimmers, and a microswimmer navigating a low-Reynolds number environment. In each case, I will show how AI-powered control enables these agents to overcome the inherent difficulties of locomotion in their respective environments. Together, these results highlight the transformative potential of AI in enabling adaptive and robust locomotion in fluid environments.

Also on zoom https://ucdavis.zoom.us/j/98969645841