Robot swarms are envisioned in applications such as surveillance, agriculture, search-and-rescue operations, and construction. The decentralized nature of swarm intelligence has three key advantages over traditional multi-robot control algorithms: it is scalable, it is fault tolerant, and it is not susceptible to a single point of failure. These advantages are critical to the task of persistent surveillance - where a number of target locations need to be visited as frequently as possible. Unfortunately, in the real world, the autonomous robots that can be used for persistent surveillance have a limited battery life (or fuel capacity). Thus, they need to abandon their surveillance duties to visit a battery swapping station (or refueling depot) a.k.a. €˜depots€™. This €˜down time€™ reduces the frequency of visitation. This problem can be eliminated if the depots themselves were autonomous vehicles that could meet the (surveillance) robots at some point along their path from one target to another. Thus, the robots would spend less time on the 'charging' (or refueling) task. In this thesis we present decentralized control algorithms, and their results, for three stages of the persistent surveillance problem. First, we consider the case where the robots have no energy constraints, and use a decentralized approach to allow the robots choose the €˜best€™ target that they should visit next. While the selection process is decentralized, the robots can communicate with all the other robots in the swarm, and let them know which is their chosen target. We then consider the energy constraints of the robots, and slightly modify the algorithm, so that the robots visit a depot before they run out of energy. Lastly, we consider the case where the depots themselves can move, and communicate with the robots to pick a location and time to meet, to be able to swap the empty battery of a robot, with a fresh one. The goal of persistent surveillance is to visit target locations as frequently as possible, and thus, the performance measurement parameter is chosen to be the median frequency of visitation for all target locations. We evaluate the performance of the three algorithms in an extensive set of simulated experiments.

Robot swarms have recently become of interest in both industry and academia for their potential to perform various difficult or dangerous tasks efficiently. As real robot swarms become more of a possibility, many desire swarms to be controlled or directed by a human, which raises questions regarding how that should be done. Part of the challenge of human-swarm interaction is the difficulty of understanding swarm state and how to drive the swarm to produce emergent behaviors. Human input could inhibit desirable swarm behaviors if their input is poor and has sufficient influence over swarm agents, affecting its overall performance. Thus, with too little influence, human input is useless, but with too much, it can be destructive. We suggest that there is some middle level, or interval, of human influence that allows the swarm to take advantage of useful human input while minimizing the effect of destructive input. Further, we propose that human-swarm interaction schemes can be designed to maintain an appropriate level of human influence over the swarm and maintain or improve swarm performance in the presence of both useful and destructive human input. We test this theory by implementing a piece of software to dynamically moderate influence and then testing it with a simulated honey bee colony performing nest site selection, simulated human input, and actual human input via a user study. The results suggest that moderating influence, as suggested, is important for maintaining high performance in the presence of both useful and destructive human input. However, while our software seems to successfully moderate influence with simulated human input, it fails to do so with actual human input.

Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency, bandwidth, operator dropout, and communication noise are significant factors, human organizations that promote individual initiative perform more effectively and resiliently than hierarchies in the cooperative best-m-of-n task. Simulations automating the behavior of hub-based swarm robotic agents and groups of human operators are used to evaluate this hypothesis. To make the comparisons between the team and hierarchies meaningful, we explore parameter values determining how simulated human operators behave in teams and hierarchies to optimize the performance of the respective organizations. We show that simulation results generally support the hypothesis with respect to the effect of latency and bandwidth on organizational performance.