Control of swarms with multiple leader agents

The study of human control of robotic swarms involves designing interfaces and algorithms for allowing a human operator to influence a swarm of robots. One of the main difficulties, however, is determining how to most effectively influence the swarm after it has been deployed. Past work has focused on influencing the swarm via statically selected leaders-swarm members that the operator directly controls. This paper investigates the use of a small subset of the swarm as leaders that are dynamically selected during the scenario execution and are directly controlled by the human operator to guide the rest of the swarm, which is operating under a flocking-style algorithm. The goal of the operator in this study is to move the swarm to goal regions that arise dynamically in the environment.We experimentally investigated three different aspects of dynamic leader-based swarm control and their interactions: leader density (in terms of guaranteed hops to a leader), sensing error, and method of information propagation from leaders to the rest of the swarm. Our results show that, while there was a large drop in the number of goals reached when moving from a 1-hop to a 2-hop guarantee, the difference between a 2-hop, 3-hop, and 4-hop guarantee was not statistically significant. Furthermore, we found that sensing error impacted the explicit information-propagation method more than the tacit method conditions, and caused participants more trouble the lower the density of leaders, although the explicit method performed better overall.