Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Designing the next generation of such swarms requires the ability to rapidly test algorithms, sensors, and support infrastructure at scale. Simulation is useful in the early stages of such large-scale system design, when hardware is unavailable or deployment at scale is impractical. To faithfully represent the problem domain, an MAV swarm simulator must be able to model all key aspects of the system:  actuation, sensing, and communication . Further, it is important to be able to quickly test swarm behavior using different control algorithms in a varied set of environments, and with a variety of sensors.   We demonstrate Simbeeotic, a simulation framework that is capable of modeling large-scale MAV swarms. Simbeeotic enables algorithm development and rapid prototyping through both simulation and hardware-in-the-loop experimentation. We demonstrate Simbeeotic running simulated applications and videos demonstrating hybrid experiments with simulated MAVs as well as helicopters flying in our testbed that show the power and versatility required to assist next generation swarm design.
['Jason Waterman', 'Bryan Kate', 'Karthik Dantu', 'Matt Welsh']
Simbeeotic: a simulation-emulation platform for large scale micro-aerial swarms