Multidisciplinary Control Co-Design Optimization of Anguilliform-Swimming Soft Fluidic Robots

Anguilliform-swimming soft fluidic robots hold great promise for a range of underwater applications. However, because they leverage the complex dynamics of soft bodies interacting with fluids, it is challenging to use intuition to determine design parameters. Multidisciplinary design optimization offers a promising solution to this challenge by providing an automated and systematic method for leveraging computational models to find optimal design parameters. This study investigates a method for multidisciplinary control co-design optimization of anguilliform-swimming soft robots, using physics-based models to optimize shape and control parameters. The modeling framework includes a geometry-centric approach for geometry modeling and parameterization, a three-dimensional finite element model for structural mechanics, and an unsteady panel method for fluid dynamics. The approach is tested by applying it to the optimization of a pre-existing eel-inspired soft robot. Model parameters are estimated from existing experimental data, and two control co-design optimizations, with high-level and multilevel shape parameterizations, are performed to minimize energy cost. The optimized actuator module is manufactured, and used to collect additional data for re-estimating the structural model parameters. The optimization is performed again with the updated model parameters, showing a simulated energy cost reduction of 45% and the prescribed 128% speed increase compared to the baseline design with optimized control and updated model parameters. These results demonstrate the potential of the proposed optimization approach to advance the performance of anguilliform-swimming soft fluidic robots.