Effects of Swarm Size Variability on Operator Workload

Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challenge is that swarm size is often dynamic: robots may join or leave the mission due to failures or redeployment, causing abrupt workload fluctuations. Understanding how such changes affect human workload and performance is critical for robust human--swarm interaction design. This paper investigates how the magnitude and direction of changes in swarm size influence operator workload. Drawing on the concept of workload history, we test three hypotheses: (1) workload remains elevated following decreases in swarm size, (2) small increases are more manageable than large jumps, and (3) sufficiently large changes override these effects by inducing a cognitive reset. We conducted two studies (N = 34) using a monitoring task with simulated drone swarms of varying sizes. By varying the swarm size between episodes, we measured perceived workload relative to swarm size changes. Results show that objective performance is largely unaffected by small changes in swarm size, while subjective workload is sensitive to both change direction and magnitude. Small increases preserve lower workload, whereas small decreases leave workload elevated, indicating workload residue; large changes in either direction attenuate these effects, suggesting a reset response. These findings offer actionable guidance for managing swarm-size transitions to support operator workload in dynamic human--swarm systems.

Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings

This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.

Adaptive Human-Swarm Interaction based on Workload Measurement using Functional Near-Infrared Spectroscopy

One of the challenges of human-swarm interaction (HSI) is how to manage the operator's workload. In order to do this, we propose a novel neurofeedback technique for the real-time measurement of workload using functional near-infrared spectroscopy (fNIRS). The objective is to develop a baseline for workload measurement in human-swarm interaction using fNIRS and to develop an interface that dynamically adapts to the operator's workload. The proposed method consists of using fNIRS device to measure brain activity, process this through a machine learning algorithm, and pass it on to the HSI interface. By dynamically adapting the HSI interface, the swarm operator's workload could be reduced and the performance improved.