The role of the dynamics of relative motion in information passing in natural and engineered collective motion

The breathtaking motions of natural groups such as bird flocks and fish schools have been a major inspiration for research in the developement of control algorithms for multi-robotic systems. A main thrust of research in this area is to understand how individual behaviors result in group behaviors. In this dissertation, we are concerned with how individuals — either natural or engineered — move dynamically relative to one another and how this affects information passing in the group, thereby impacting the performance of the group when completing tasks.;The work presented in this dissertation is inspired by observations of coordinated speed oscillations in schools of banded killifish (Fundulus diaphanus). We analyze trajectory data of schools of two and three killifish and show that their speed oscillations produce periodic relative motion that modulates line-of-sight visibility between fish. We leverage tools from graph theory and dynamical models of consensus decision making to show that this phenomenon can significantly improve group decision-making performance. We also investigate the potential benefits dynamic relative motion for engineered systems, particularly mobile sensor networks. We investigate the ways that relative motion can enhance mobile sensor networks with respect to group connectivity and decision-making performance. We also derive control laws to stabilize groups of mobile agents that are engaged in killifish-like coordinated speed oscillations into a rich family of formations along circular trajectories. We present design guidelines for these types of formations and algorithms to compute what speed oscillation waveform will produce or approximate a formation with a given shape. Motivated by the desire to perform controlled experiments that can further illuminate these and other themes related to collective motion in animal groups, we present the design of a testbed in which a robotic fish interacts with a school of live fish in real time. Each robotic fish consists of a wheeled robot and a model fish; the model fish is moved about a shallow tank of water via magnetic coupling to a wheeled robot beneath the tank. A significant part of the design of this testbed is the real-time computer vision tracking of the robotic fish and the school of fish. The technology that we have developed for the robotic fish testbed is relevant to a number of other applications.