Human Control of Robot Swarms with Dynamic Leaders

Abstract

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. This may be necessary in a environmental exploration task where areas of interest arise dynamically, and thus the human operator needs to guide the swarm to explore them. Past work has focused on influencing the swarm via statically selected leaders—swarm members that the operator directly controls. These leaders have been pre-selected and remain leaders throughout the scenario execution. This paper investigates the use of a small subset of the swarm as robot 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 (a) the effect of density of leaders on the ease of human control and system performance, and (b) how restriction of information communicated to the human operator affects the ability to guide the swarm to goal regions. The density of leaders is computed based on an extension of the RCC algorithm used in wireless sensor networks to select cluster heads. We used a “hop guarantee” in this leader selection algorithm as a measure of leader density. A n-hop guarantee means that every robot is at most n-hops away from a leader. In particular, we studied the effect of 1-hop, 2-hop and 3-hop guarantee on the swarm performance. 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 and 3- hop guarantee was not statistically significant. Furthermore, we found that performance was just as good when the information returned to the operator was restricted, showing that operators can still navigate a swarm even when they have imperfect information.