3 papers indexed
Pavlo Kupyn, Yuya Hamamatsu, Roza Gkliva, Asko Ristolainen, Maarja Kruusmaa
This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. We focus on morphologically similar robots that differ in scale and hydrodynamic properties. A model trained on data from a larger robot (sour…
Yuya Hamamatsu, Maarja Kruusmaa, Asko Ristolainen
In this study, we propose a layout-independent control allocator capable of zero-shot deployment across diverse actuator configurations. The proposed method utilizes a learning pipeline that integrates a Graph Neural Network (GNN) and a Transformer to represent the robot's geomet…
Yuya Hamamatsu, Zixi Chen, Maarja Kruusmaa, Asko Ristolainen
Efficient flapping propulsion hinges on operating within a narrow Strouhal number window, a principle nature has converged upon for maximum thrust-to-power ratio. We translate this bioinspired empirical rule into real-time control, demonstrating it on an autonomous underwater veh…