Human control of robot swarms with dynamic leaders

Abstract

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.

A Survey on Aerial Swarm Robotics

“…This is no different conceptually from the usual setting of an autonomous swarm, since it involves the human acting essentially outside the algorithmic loop. It has been argued that humans are able to guide a swarm better using a dynamic set of leaders [143], as compared to manipulating a fixed leader. There is also evidence which suggests that human operators can adapt their handling of (virtual) leaders to guide large swarms through obstacle-rich environments in a better manner than built-in, standard flocking rules [144].…”

Human Interaction With Robot Swarms: A Survey

“…Here, the authors found that the explicit method gave human operators better control over the swarm, but hypothesized that the tacit method could be more robust to sensing error if a larger percentage of the swarm were leader robots, to allow for faster propagation of used intent. In [119] and [120], the authors further this work by presenting an algorithm for selecting multiple leaders dynamically in a swarm as the topology of the communication graph changes. They found that, while the explicit method of propagation was again superior overall, the tacit method performed better under significant sensing error.…”

A review of swarm robotics tasks

“…The technique proposed in [183] consists in taking control of one robot in the swarm, transforming it in an avatar, so that local modifications obtained with a changed behavior of the avatar are transferred gradually to the rest of the swarm. A similar method is used by Walker et al [184], who investigated how a swarm can be controlled by dynamically selecting leader robots and guiding their movement. Kolling et al [185] proposed two types of interaction, called intermittent and environmental : the first type consists in selecting individual robots to make them switch from their current behavior to a new behavior; with the second type, users manipulate a local characteristic of the environment in order to induce a new behavior in robots located in the nearby area.…”