In this thesis we propose methods and metrics for human interaction with bio-inspired robot teams. We refine the concept of a stakeholder and demonstrate how a human can use stakeholders to lead a swarm as well as switch the swarm between different collective behaviors. We extend the human interaction metrics of interaction time and interaction effort presented in [1] to swarm systems and introduce the concept of interaction effort. These metrics allow us to understand how well the system performs under human influence. We employ systems theory to estimate these metrics, which is useful because this can be done without performing user studies.

Robots, including UAVs, have found increasing use in helping humans with dangerous and difficult tasks. The number of robots in use is increasing and is likely to continue increasing in the future. As the number of robots increases, human operators will need to coordinate and control the actions of large teams of robots. While multi-robot supervisory control has been widely studied, it requires that an operator divide his or her attention between robots. Consequently, the use of multi-robot supervisory control is limited by the number of robots that a human or team of humans can reasonably control. Swarm robotics -- large numbers of low-cost robots displaying collective behaviors -- offers an alternative approach by providing the operator with a small set of inputs and parameters that alter the behavior of a large number of autonomous or semi-autonomous robots. Researchers have asserted that this approach is more scalable and offers greater promise for managing huge numbers of robots. The emerging field of Human-Swarm Interaction (HSI) deals with the effective management of swarms by human operators. In this thesis we offer foundational work on the effect of HSI (a) on the individual robots, (b) on the group as a whole, and (c) on the workload of the human operator. We (1) show that existing general swarm algorithms are feasible on existing robots and can display collective behaviors as shown in simulations in the literature, (2) analyze the effect of interaction style and neighborhood type on the swarm's topology, (3) demonstrate that operator workload stays stable as the size of the swarm increases, but (4) find that operator workload is influenced by the interaction style. We also present considerations for swarm deployment on real robots.