Fine Tuning Swimming Locomotion Learned from Mosquito Larvae

2024/11/16 by Pranav Rajbhandari, Rajbhandari, Pranav, Karthick Dhileep +5
Engineering · #Artificial Intelligence (cs.AI) #Biomimetic flight and propulsion mechanisms #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Robotic Locomotion and Control

paper · pdf · doi:10.48550/arxiv.2412.02702

openalex publication_date 2024/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In prior research, we analyzed the backwards swimming motion of mosquito larvae, parameterized it, and replicated it in a Computational Fluid Dynamics (CFD) model. Since the parameterized swimming motion is copied from observed larvae, it is not necessarily the most efficient locomotion for the model of the swimmer. In this project, we further optimize this copied solution for the swimmer model. We utilize Reinforcement Learning to guide local parameter updates. Since the majority of the computation cost arises from the CFD model, we additionally train a deep learning model to replicate the forces acting on the swimmer model. We find that this method is effective at performing local search to improve the parameterized swimming locomotion.

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