SugarMap: Location-less coverage for micro-aerial sensing swarms

Micro-aerial vehicle (MAV) swarms are emerging as a new class of mobile sensor networks with many potential applications such as urban surveillance, disaster response, radiation monitoring, etc., where the swarm is tasked with collaboratively covering a hazardous unknown environment. However, efficient collaborative coverage is challenging due to limited individual sensing, computing and communication resources of MAV sensor nodes, and lack of location infrastructure in the unknown application environment. We present SugarMap, a novel system that enables such resource-constrained MAV nodes to achieve efficient sensing coverage. The self-establishing system uses approximate motion models of mobile nodes in conjunction with radio signatures from self-deployed stationary anchor nodes to create a common coverage map. Consequently, the system coordinates node movements to reduce sensing overlap and increase the speed and efficiency of coverage. The system uses particle filters to account for uncertainty in sensors and actuation of MAV nodes, and incorporates redundancy to guarantee coverage. Through large-scale simulations and a real implementation on the SensorFly MAV sensing platform, we show that SugarMap provides better coverage than the existing coverage approaches for MAV swarms.