Adaptive Planning for Deployment of Micro-Aerial Sensor Swarms

Micro-aerial vehicle (MAV) swarms are a new class of mobile sensor networks with many applications including search and rescue, urban surveillance, radiation monitoring, etc. These sensing applications require autonomously deploying a high number of low-cost, lowcomplexity MAV sensor nodes at suitable locations in hazardous environments. We propose a collaborative algorithm for resource-constrained MAV nodes to quickly and efficiently deploy at preassigned locations in multiroom scenarios. Through large-scale simulations we show that the proposed technique provides significant benefit over existing autonomous deployment strategies.