Analysis of Human-Swarm Visualizations

摘要

Interest in robotic swarms has increased exponentially. Prior research determined that humans perceive biological swarm motions as a single entity, rather than perceiving the individuals. An open question is how the swarm’s visual representation and the associated task impact human performance when identifying current swarm tasks. The majority of the existing swarm visualizations present each robot individually. Swarms typically incorporate large numbers of individuals, where the individuals exhibit simple behaviors, but the swarm appears to exhibit more intelligent behavior. As the swarm size increases, it becomes increasingly difficult for the human operator to understand the swarm’s current state, the emergent behaviors, and predict future outcomes. Alternative swarm visualizations are one means of mitigating high operator workload and risk of human error. Five visualizations were evaluated for two tasks, go to and avoid, in the presence or absence of obstacles. The results indicate that visualizations incorporating representations of individual agents resulted in higher accuracy when identifying tasks.