Path Planning Algorithm to Enable Low Altitude Delivery Drones at the City Scale

The world of shipping business is expecting a transformation in the near future due to the rise of delivery via Unmanned Aerial Vehicles (UAV), commonly known as drones. However, the mass adoption of UAVs for delivery purposes faces in urban and suburban areas faces certain challenges that are unique and currently is an area of active research. For the reliable city-scale operation of delivery drones which are a swarm of autonomous Unmanned Aircraft Systems (UAS) operating at low altitude airspace outside line of sight, reliable algorithms to avoid obstacles, geofenced structures, and other drone traffic is absolutely necessary. This paper presents an improvement over state of the art on a path-planning algorithm that enables UAS to fly a designated mission factoring in geofencing and real-time traffic. This planning algorithm relies on a rapidly exploring random tree methodology to maintain clearance from other drone traffic and geofenced objects. Heuristic-based termination criteria for tree expansion allow for low computation times, which is a good fit for UAVs with limited computing capability onboard.