Robotic fish are underwater robots that emulate locomotion of live fish through actuated fin and/or body movements. They are of increasing interest due to their potential applications such as aquatic environmental monitoring and robot-animal interactions.In this work, several bio-inspired robotic fish prototypes have been developed that make use of periodic tail motions. A dynamic model for a tail-actuated robotic fish is presented by merging rigid-body dynamics with Lighthill's large-amplitude elongated-body theory. The model is validated with extensive experiments conducted on a robotic fish prototype. The role of incorporating the body motion in evaluating the tail-generated hydrodynamic forces is assessed, which shows that ignoring the body motion (as often done in the literature) results in significant overestimate of the thrust force and robot speed. By exploiting the strong correlation between the angle of attack and the tail-beat bias, a computationally efficient approach is further proposed to adapt the drag coefficients of the robotic fish.It has been recognized that the flexibility of the body and fin structures has a pronounced impact on the swimming performance of biological and robotic fish. To analyze and utilize this trait, a novel dynamic model is developed for a robotic fish propelled by a flexible tail actuated at the base. The tail is modeled with multiple rigid segments connected in series through rotational springs and dampers. For comparison, a model using linear beam theory is created to capture the beam dynamics. Experimental result show that the two models have almost identical predictions when the tail undergoes small deformation, but only the proposed multi-segment model matches the experimental measurement closely for all tail motions.Motivated by the need for system analysis and efficient control of robotic fish, averaging of robots' dynamics is of interest. For dynamic models of robotic fish, however, classical or geometric averaging typically cannot produce an average model that is accurate and the in the meantime amenable to analysis or control design. In this work, a novel averaging approach for tail-actuated robotic fish dynamics is proposed. The approach consists of scaling the force and moment terms and then conducting classical averaging. Numerical investigation reveals that the scaling function for the force terms is a constant independent of tail-beat patterns, while the scaling function for the moment term depends linearly on the tail-beat bias. Existence and local stability of the equilibria for the average model are further analyzed. Finally, as an illustration of the utility of the average model, a semi-analytical framework is presented for obtaining steady turning parameters.Sampling and reconstruction of a physical field using mobile sensor networks have recently received significant interest. In this work, an adaptive sampling framework is proposed to reconstruct aquatic environmental fields (e.g., temperature, or biomass of harmful algal blooms) using schools of robotic sensor platforms. In particular, it is assumed that the field of interest can be approximated by a low rank matrix, which is exploited for successive expansion of sampling area and analytical reconstruction of the field. For comparison, an Augmented Lagrange Multiplier optimization approach is also taken to complete the matrix reconstruction using a limited number of samples. Simulation results show that the proposed approach is more computationally efficient and requires shorter travel distances for the robots.
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