Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms

Abstract

This research explores a future concept requiring the efficient and safe, landing and recovery of a swarm of unmanned aerial vehicles (UAVs). The presented work involves the use of an overarching (centralized) airspace optimization model, formulated analytically as a network-based model with side constraints describing a time-expanded network model of the terminal airspace in which the UAVs navigate to one or more (possibly moving) landing zones. This model generates optimal paths in a centralized manner such that the UAVs are properly sequenced into the landing areas. The network-based model is grown using agent based simulation with simple flocking rules. The resulting solution is compared to another agent-based model which uses similar avoidance rules for the landing of these UAVs, exploring the benefit of distributed computation and decision-making characteristic of swarming models. Relevant measures of performance include, e.g., the total time necessary to land the swarm. Extensive simulation studies and sensitivity analyses are conducted to demonstrate the relative effectiveness of the proposed approaches.