  The control of collective robotic systems, such as drone swarms, is often
delegated to autonomous navigation algorithms due to their high dimensionality.
However, like other robotic entities, drone swarms can still benefit from being
teleoperated by human operators, whose perception and decision-making
capabilities are still out of the reach of autonomous systems. Drone swarm
teleoperation is only at its dawn, and a standard human-swarm interface (HRI)
is missing to date. In this study, we analyzed the spontaneous interaction
strategies of naive users with a swarm of drones. We implemented a
machine-learning algorithm to define a personalized Body-Machine Interface
(BoMI) based only on a short calibration procedure. During this procedure, the
human operator is asked to move spontaneously as if they were in control of a
simulated drone swarm. We assessed that hands are the most commonly adopted
body segment, and thus we chose a LEAP Motion controller to track them to let
the users control the aerial drone swarm. This choice makes our interface
portable since it does not rely on a centralized system for tracking the human
body. We validated our algorithm to define personalized HRIs for a set of
participants in a realistic simulated environment, showing promising results in
performance and user experience. Our method leaves unprecedented freedom to the
user to choose between position and velocity control only based on their body
motion preferences.

Robotics;Human-Computer Interaction