Typical visualizations of robot swarms (greater than 50 entities) display each individual entity; however, it is immensely difficult to maintain accurate position information for each member in real-world situations with limited communications. Generally, it will be difficult for humans to maintain an awareness of all individual entities. Further, the swarm's tasks may impact the desired visualization. Thus, an open question is how best to visualize a swarm given various swarm tasks. This paper presents a heuristic evaluation that analyzes the application of swarm metrics to different swarm visualizations and tasks. A brief overview of the visualizations is provided, along with a description of the heuristic metrics and the analysis.

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.