A Graph Neural Network (GNN) is a type of neural network architecture that operates on data consisting of objects and their relationships, which are represented by a graph. Within the graph, nodes represent objects and edges represent associations between those objects. The representation of relationships and correlations between data is unique to graph structures. GNNs exploit this feature of graphs by augmenting both forms of data, individual and relational, and have been designed to allow for communication and sharing of data within each neural network layer. These benefits allow each node to have an enriched perspective, or a better understanding, of its neighbouring nodes and its connections to those nodes. The ability of GNNs to efficiently process high-dimensional node data and multi-faceted relationships among nodes gives them advantages over neural network architectures such as Convolutional Neural Networks (CNNs) that do not implicitly handle relational data. These quintessential characteristics of GNN models make them suitable for solving problems in which the correspondences among input data are needed to produce an accurate and precise representation of these data. GNN frameworks may significantly improve existing communication and control techniques for multi-agent tasks by implicitly representing not only information associated with the individual agents, such as agent position, velocity, and camera data, but also their relationships with one another, such as distances between the agents and their ability to communicate with one another. One such task is a multi-agent navigation problem in which the agents must coordinate with one another in a decentralized manner, using proximity sensors only, to navigate safely to their intended goal positions in the environment without collisions or deadlocks. The contribution of this thesis is the design of an end-to-end decentralized control scheme for multi-agent navigation that utilizes GNNs to prevent inter-agent collisions and deadlocks. The contributions consist of the development, simulation and evaluation of the performance of an advantage actor-critic (A2C) reinforcement learning algorithm that employs actor and critic networks for training that simultaneously approximate the policy function and value function, respectively. These networks are implemented using GNN frameworks for navigation by groups of 3, 5, 10 and 15 agents in simulated two-dimensional environments. It is observed that in $40\%$ to $50\%$ of the simulation trials, between 70$\%$ to 80$\%$ of the agents reach their goal positions without colliding with other agents or becoming trapped in deadlocks. The model is also compared to a random run simulation, where actions are chosen randomly for the agents and observe that the model performs notably well for smaller groups of agents.

Entrepreneurship is an incredibly difficult endeavor. Along with the potentially high risk-to-return-ratio, starting an entrepreneurial venture, in nearly any capacity, necessitates a significant summation of work, time, creativity, and adaptability.1 In my opinion, many of the token hyper-productive individuals that have produced enormous amounts of value for the world were entrepreneurs who started their own companies and organizations. However, for every successful founder, there are thousands of failed entrepreneurs. In 2015, the Bureau of Labor Statistics found that roughly 50% of businesses fail in the first four years.2 Founders, over time, must become professionals in their respective industries in order to succeed. With limited financial capital to hire employees, founders must learn skills in a variety of focus areas which could include finance, strategy, technology, management, marketing, sales, and many more, until they can generate enough capital to hire employees to fulfill these roles. Although the learnings and experiences from starting a company can more effectively be understood through experiencing it first-hand, in this document, I intend to start a startup from scratch, learn a multitude of skills involved with starting a startup and describe my experience. My hope is that potential founders can read this document and get a better understanding of what it’s like to start a startup. This thesis will be less focused on quantitative data capturing and more focused on my first-hand experience.

In this project, an existing waveform generator designed by the vagus nerve stimulation (VNS) technology firm Hoolest Performance Technologies was modified and characterized. Voltage feedback and current feedback systems were designed in order to improve output voltage and current regulation. A wireless communication system was implemented onboard the newly designed waveform generator in order to improve user experience and allow the system to be controlled remotely. Finally, a custom printed circuit board was designed according to the established circuit schematics for the above components, and the layout was miniaturized to a total board footprint area of 1.5 square inches. The completed device was characterized according to several figures of merit including current consumption, voltage and current regulation, and short-circuit behavior.

Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish their battery reserves, using a contact charging point. This system is designed to work with all types of rechargeable batteries, focusing on Lithium Polymer (LiPo) packs, that incorporate a battery management system for increased reliability. The project also explores optimization methods for fleets of UAV systems, to increase charging efficiency and extend battery lifespans. Each component of this project was first designed and tested in computer simulation. Following positive feedback and results, prototypes for each part of this system were developed and rigorously tested. Results show that the contact charging method is able to charge LiPo batteries at a 1-C rate, which is the industry standard rate, maintaining the same safety and efficiency standards as modern day direct connection chargers. Control software for these base stations was also created, to be integrated with a fleet management system, and optimizes UAV charge levels and distribution to extend LiPo battery lifetimes while still meeting expected mission demand. Each component of this project (hardware/software) was designed for manufacturing and implementation using industry standard tools, making it ideal for large-scale implementations. This system has been successfully tested with a fleet of UAV systems at Arizona State University, and is currently being integrated into an Arizona smart city environment for deployment.