Fish are capable of producing a wide repertoire of 3D propulsive forces using their fins, and have inspired the development of compliant, multiple-DOF, robotic fins with similar capabilities. Most of these robotic fins are under open-loop control on propulsive force because the forces are challenging to model. Understanding how to predict propulsive forces for these types of fins would significantly advance the state of the art towards closed-loop control of forces. Distributed sensors within robotic fins have been used to predict propulsive forces using linear models, but these models fail to predict forces when fin kinematics become more complex. The objective of the work presented herein is to understand the use of nonlinear, multiple-input-single-output (MISO) Volterra series models between intrinsic sensory measurements and propulsive forces of a flexible robotic fin. Techniques in nonlinear system identification are used to address model conditioning. Nonlinear models predict the propulsive forces well, capturing features of both thrust and lateral forces. Nonlinear models significantly outperformed linear models both in cost of implementation and performance. The best sensor sampling practice was to sample from multiple locations with both pressure and bending modalities. Distributed sensing paired with nonlinear Volterra series models was successful for predicting the forces created by flexible robotic fins with complex kinematics and multiple degrees of freedom.

Biologically-inspired robotic (biorobotic) platforms have been successfully adapted for engineering use, but it is difficult to extend these platforms' locomotive gaits to meet optimization goals. The gait spaces of biorobotic platforms can be very large, with multiple local optima and intractable numerical models. Further, the time cost of empirical exploration is often prohibitive. Micro-genetic algorithms have been successful in developing inverse kinematics in simulation, optimizing in spaces with numerous local optima, and working quickly to optimize with low numbers of trials, but have not yet been evaluated for online robotic gait development. To address the problem of engineering gait development in a biorobotic space, a micro-genetic algorithm (μGA) is evaluated on a biorobotic pectoral fin platform. The μGA effectively optimizes in the gait space with low time costs, discovering new gaits that optimize thrust force production on the swimming fin. The μGA also reveals parameter tuning strategies for changing propulsive forces. Overall, the μGA framework is shown to be effective at online optimization in a large, complex biorobotic gait space.

A comprehensive understanding of the ways in which fish create and control forces is fundamental to engineering underwater vehicles that maneuver with the agility of fish. In this study the sunfish was selected as a biological model from which to understand pectoral fin motions and forces during hover. The kinematic patterns of the biological fin were identified and implemented on a biorobotic model of the fin. The effects of fin patterns and mechanical properties on force were evaluated. Pressure was measured at multiple points on the fin's surface and assessed for use in the closed loop control of fin force. The study revealed that a wide range of motions are used during hover, and that forces are significantly different from those found previously for steady swimming. However as fin speeds increase, the fin's dynamic motions, and the magnitude and direction of the forces become more similar to those of steady swimming. Collective measures of pressure over the fin's surface exhibited trends that correlated well with fin forces in relative magnitudes and directions. Results strongly suggest that distributed measures of pressure are useful for force prediction and control.

The remarkable swimming abilities of bony fish are the result of multiple interacting subsystems, each tuned to perform certain roles. These subsystems, which include the statically unstable body, multiple highly actuated fins, oscillatory neural controllers, and distributed senses, are not often studied as mutually dependent systems. This research program is developing biorobotic models of these systems and integrating the systems into a biorobotic fish so that interdependencies can be explored during free swimming. The robot body was derived from a bluegill sunfish, and has a tunable mass distribution and a mixture of rigid and flexible sections so that dynamical characteristics of the fish body can be explored. Five highly deformable fins have structural properties scaled to those of biological fins and can create gait patterns for steady swimming and maneuvers. A first generation artificial CPG has been programmed for each fin on a network of five low power microcontrollers. Finally, a dedicated biorobotic pectoral fin has been developed and instrumented with distributed sensory systems so relevant physical (e.g., fin curvature) and hydrodynamic (e.g., pressure) data can be identified and used to predict fin force for closed loop control.