Towards evaluating the impact of swarm robotic control strategy on operators’ cognitive load

Abstract

The use of multi-robot systems is increasing in disaster response, industry, transport, and logistics. Humans will remain indispensable to control and manage these fleets of robots, particularly in safety-critical applications. However, a human operator’s cognitive capacities can be challenged and exceeded as the sizes of autonomous fleets grow, and more sophisticated AI techniques can lead to opaque robot control programs. In a user study (n = 40), we explore how autonomous swarm intelligence algorithms and novel tangible interaction modalities relate to subjective and physiological indices of operator cognitive load (i.e., NASA Task Load Index, heart rate variability). Our findings suggest that there are differences in workload across conditions; however, subjective and cardiac measures appear to be sensitive to different aspects of cognitive state. The results hint at the potential of both tangible interfaces and automation to engage operators and reduce cognitive load, yet show the need for further validation of workload measures for use in studying and optimizing human-swarm interactions.