Tsunami: Scalable, Fault Tolerant Coverage Path Planning for UAV Swarms

Unmanned Aerial Vehicles are powerful robotic tools capable of quickly sensing vast areas. Their maneuverability and speed suit them to sensing tasks in diverse domains including agriculture, search and rescue, infrastructure inspection, and ecology. Piloting these missions, however, is expensive, time consuming, and requires expertise. Furthermore, environments for sensing can be large, necessitating swarms of UAV for timely scouting. Waypoint-based automated collection systems have existed for over a decade, but rarely account for the complexities of UAV swarms, including faults regularly experienced on long deployments. In this paper, we introduce Tsunami, a novel system for UAV swarm data collection. Tsunami dynamically partitions environments, avoids aerial collisions, and responds to changes in swarm size over the course of long missions due to battery discharges and faults. Through a simulated agricultural case study, we show that Tsunami is efficient, fault tolerant, and improves data capture time by 1.6-1.91X when compared to state of the art coverage path planning algorithms.