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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. 

The earth’s orbit is becoming increasingly crowded with debris that poses significant safety risks to the operation of existing and new spacecraft and satellites. The active tether-net system, which consists of a flexible net with maneuverable corner nodes, launched from a small autonomous spacecraft, is a promising solution to capturing and disposing of such space debris. The requirement of autonomous operation and the need to generalize over debris scenarios in terms of different rotational rates makes the capture process significantly challenging. The space debris could rotate about multiple axes, which along with sensing/estimation and actuation uncertainties, call for a robust, generalizable approach to guiding the net launch and flight – one that can guarantee robust capture. This paper proposes a decentralized actuation system combined with reinforcement learning based on prior work in designing and controlling this tether-net system. In this new system, four microsatellites with thrusters act as the corner nodes of the net, and can thus help control the flight of the net after launch. The microsatellites pull the net to complete the task of approaching and capturing the space debris. The proposed method uses a reinforcement learning framework that integrates a proximal policy optimization to find the optimal solution based on the dynamics simulation of the net and the MUs in Vortex Studio. The reinforcement learning framework finds the optimal trajectory that is both energy-efficient and ensures a desired level of capture quality