Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Simulation and staged deployment to prototype testbeds are useful in the early stages of large-scale system design, when hardware is unavailable or deployment at scale is impractical. To faithfully represent the problem domain, a MAV swarm simulator must be able to model the key aspects of the system: actuation, sensing, and communication. We present Simbeeotic, a simulation framework geared toward modeling swarms of MAVs. Simbeeotic enables algorithm development and rapid MAV prototyping through pure simulation and hardware-in-the-loop experimentation. We demonstrate that Simbeeotic provides the appropriate level of fidelity to evaluate prototype systems while maintaining the ability to test at scale.

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 compare our previously developed deterministic [7] and stochastic [3], [4] strategies for allocating tasks in robotic swarms1 consisting of very large populations of highly resource-constrained robots. We study our two task allocation approaches in a simulated scenario in which a collective of insect-inspired micro-aerial vehicles (MAVs) must produce a specified spatial distribution of pollination activity over a crop field. We investigate the approaches' requirements, advantages, and disadvantages under realistic conditions of error in robot localization, navigation, and sensing in simulation. Our results show that the deterministic approach, which requires region-based robot navigation, yields higher task progress in all cases. For robots without such navigation capabilities, the stochastic approach is a feasible alternative, and its resulting task progress is less sensitive to error in localization, error in navigation, and a combination of high error in localization, navigation, and sensing.

Research in micro-aerial vehicle (MAV) construction, control, and high-density power sources is enabling swarms of MAVs as a new class of mobile sensing systems. For efficient operation, such systems must adapt to dynamic environments, cope with uncertainty in sensing and control, and operate with limited resources. We propose a novel system architecture based on a hive-drone model that simplifies the functionality of an individual MAV to a sequence of sensing and actuation commands with no in-field communication. This decision simplifies the hardware and software complexity of individual MAVs and moves the complexity of coordination entirely to a central hive computer. We present Karma, a system for programming and managing MAV swarms. Through simulation and testbed experiments we demonstrate how applications in Karma can run on limited resources, are robust to individual MAV failure, and adapt to changes in the environment.