Dynamics and Control of Nonholonomic Systems with Internal Degrees of Freedom

Nonholonomic systems model many robots as well as animals and other systems. Although such systems have been studied extensively over the last century, much work still remains to be done on their dynamics and control. Many techniques have been developed for controlling kinematic nonholonomic systems or simplified dynamic versions, however control of high dimensional, underactuated nonholonomic systems remains to be addressed. This dissertation helps fill this gap by developing a control algorithm that can be applied to systems with three or more configuration variables and just one input. We also analyze the dynamic effects of passive degrees of freedom and elastic potentials which are commonly observed in such systems showing that the addition of a passive degree of freedom can even be used to improve the locomotion characteristics of a system. Such elastic potentials can be present due to compliant mechanisms or origami, both of which can exhibit bistability and many other properties that can be useful in the design of robots

Improving Swimming Performance and Flow Sensing by Incorporating Passive Mechanisms

As water makes up approximately 70% of the Earth\u27s surface, humans have expanded operations into aquatic environments out of both necessity and a desire to gain potential innate benefits. This expansion into aquatic environments has consequently developed a need for cost-effective and safe underwater monitoring, surveillance, and inspection, which are missions that autonomous underwater vehicles are particularly well suited for. Current autonomous underwater vehicles vastly underperform when compared to biological swimmers, which has prompted researchers to develop robots inspired by natural swimmers. One such robot is designed, built, tested, and numerically simulated in this thesis to gain insight into the benefits of passive mechanisms and the development of reduced-order models. Using a bio-inspired robot with multiple passive tails I demonstrate herein the relationship between maneuverability and passive appendages. I found that the allowable rotation angle, relative to the main body, of the passive tails corresponds to an increase in maneuverability. Using panel method simulations I determined that the increase in maneuverability was directly related to the change in hydrodynamic moment caused by modulating the circulation sign and location of the shed vortex wake. The identification of this hydrodynamic benefit generalizes the results and applies to a wide range of robots that utilize vortex shedding through tail flapping or body undulations to produce locomotion. Passive appendages are a form of embodied control, which manipulates the fluid-robot interaction and analogously such interaction can be sensed from the dynamics of the body. Body manipulation is a direct result of pressure fluctuations inherent in the surrounding fluid flow. These pressure fluctuations are unique to specific flow conditions, which may produce distinguishable time series kinematics of the appendage. Using a bio-inspired foil tethered in a water tunnel I classified different vortex wakes with the foil\u27s kinematic data. This form of embodied feedback could be used for the development of control algorithms dedicated to obstacle avoidance, tracking, and station holding. Mathematical models of autonomous vehicles are necessary to implement advanced control algorithms such as path planning. Models that accurately and efficiently simulate the coupled fluid-body interaction in freely swimming aquatic robots are difficult to determine due, in part, to the complex nature of fluids. My colleagues and I approach this problem by relating the swimming robot to a terrestrial vehicle known as the Chaplygin sleigh. Using our novel technique we determined an analogous Chaplygin sleigh model that accurately represents the steady-state dynamics of our swimming robot. We additionally used the subsequent model for heading and velocity control in panel method simulations. This work was inspired by the similarities in constraints and velocity space limit cycles of the swimmer and the Chaplygin sleigh, which makes this technique universal enough to be extended to other bio-inspired robots

Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments