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Abstract For a wide variety of envisioned humanitarian and commercial applications that involve a human user commanding a swarm of robotic systems, developing human-swarm interaction (HSI) principles and interfaces calls for systematic virtual environments to study such HSI implementations. Specifically, such studies are fundamental to achieving HSI that is operationally efficient and can facilitate trust calibration through the collection-use-modeling of cognitive information. However, there is a lack of such virtual environments, especially in the context of studying HSI in different operationally relevant contexts. Building on our previous work in swarm simulation and computer game-based HSI, this paper develops a comprehensive virtual environment to study HSI under varying swarm size, swarm compliance, and swarm-to-human feedback. This paper demonstrates how this simulation environment informs the development of an indoor physical (experimentation) environment to evaluate the human cognitive model. New approaches are presented to simulate physical assets based on physical experiment-based calibration and the effects that this presents on the human users. Key features of the simulation environment include medium fidelity simulation of large teams of small aerial and ground vehicles (based on the Pybullet engine), a graphical user interface to receive human command and provide feedback (from swarm assets) to human in the case of non-compliance with commands, and a lab-streaming layer to synchronize physiological data collection (e.g., related to brain activity and eye gaze) with swarm state and human commands. 

Dynamic strain based atomic force microscopy (AFM) modes often fail at the interfaces where the most interesting physics occurs because of their incapability of tracking contact resonance accurately under rough topography. To overcome this difficulty, we develop a high-throughput sequential excitation AFM that captures contact dynamics of probe–sample interactions with high fidelity and efficiency, acquiring the spectrum of data on each pixel over a range of frequencies that are excited in a sequential manner. Using electrochemically active granular ceria as an example, we map both linear and quadratic electrochemical strain accurately across grain boundaries with high spatial resolution where the conventional approach fails. The enhanced electrochemical responses point to the accumulation of small polarons in the space charge region at the grain boundaries, thought to be responsible for the enhanced electronic conductivity in nanocrystalline ceria. The spectrum of data can be processed very efficiently by physics-informed principal component analysis (PCA), speeding data processing by several orders of magnitude. This approach can be applied to a variety of AFM modes for studying a wide range of materials and structures on the nanoscale.