Human-Swarm Interactions Based on Managing Attractors

Leveraging the abilities of multiple affordable robots as a swarm is enticing because of the resulting robustness and emergent behaviors of a swarm. However, because swarms are composed of many different agents, it is difficult for a human to influence the swarm by managing individual agents. Instead, we propose that human influence should focus on a managing the higher level attractors of the swarm system and b managing trade-offs that appear in mission-relevant performance. We claim that managing attractors theoretically allows a human to abstract the details of individual agents and focus on managing the collective as a whole. Using a swarm model with two attractors, we demonstrate this concept by showing how limited human influence can cause the swarm to switch between attractors. We further claim that using quorum sensing allows a human to manage tradeoffs between the scalability of interactions and mitigating the vulnerability of the swarm to agent failures. We have presented a model of swarming that has two emergent behaviors a ock and a torus. We also provided evidence that these behaviors are fundamental attractors of the swarm dynamics. Because these behaviors are attractors, a human operator can interact with the swarm by managing these attractors. We propose that human-swarm interactions should focus on managing higher level attractors of the swarm systems because it allows a human to abstract the details of individual agents and focus on managing the collective as a whole. We extended this work by presenting an application of quorum sensing to human-swarm interactions that increases the scalability of human-swarm interactions as well as provides a mechanism for allowing a human to balance a trade-o between vulnerability and responsiveness of the swarm to agent failures. Both the stakeholder and the quorum sensing models demonstrate the ability for a human to manage a swarm by managing its emergent behaviors. Future work should improve our static