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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. 

Abstract Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.