PANDA: Priority-Based Collision Avoidance Framework for Heterogeneous UAVs Navigating in Dense Airspace

With increasing unmanned aerial vehicle (UAV) applications, the airspace is expected to be crowded with heterogeneous (quadrotors, fixed wings and hybrid UAVs) UAVs sharing a dense airspace. In this complex airspace, efficient collision avoidance techniques that respect right-of-way rules are essential. Further, UAVs may have different priorities depending on their tasks e.g. medical, logistics, etc. Due to this coupling of right-of-way with priority, collision avoidance in dense airspace becomes challenging. In this paper, we propose PANDA, a novel potential-field based approach that addresses these constraints in a unified way. Simulations show that PANDA achieves 21% faster completion time for the highest priority UAVs over a no priority baseline and a 60% faster completion time over the lowest priority UAVs.