TY - GEN
T1 - Simbeeotic
T2 - 11th ACM/IEEE Conference on Information Processing in Sensing Networks, IPSN'12
AU - Waterman, Jason
AU - Kate, Bryan
AU - Dantu, Karthik
AU - Welsh, Matt
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Micro-aerial vehicle
KW - Simulation
KW - Swarm
KW - Testbed
UR - https://www.scopus.com/pages/publications/84860528333
U2 - 10.1145/2185677.2185717
DO - 10.1145/2185677.2185717
M3 - Conference contribution
AN - SCOPUS:84860528333
SN - 9781450312271
T3 - IPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks
SP - 139
EP - 140
BT - IPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks
Y2 - 16 April 2012 through 20 April 2012
ER -