  In this paper, we investigate the integration of drone identification data
(Remote ID) with collision avoidance mechanisms to improve the safety and
efficiency of multi-drone operations. We introduce an improved Near Mid-Air
Collision (NMAC) definition, termed as UAV NMAC (uNMAC), which accounts for
uncertainties in the drone's location due to self-localization errors and
possible displacements between two location reports. Our proposed uNMAC-based
Reciprocal Velocity Obstacle (RVO) model integrates Remote ID messages with RVO
to enable enhanced collision-free navigation. We propose modifications to the
Remote ID format to include data on localization accuracy and drone airframe
size, facilitating more efficient collision avoidance decisions. Through
extensive simulations, we demonstrate that our approach halves mission
execution times compared to a conservative standard Remote ID-based RVO.
Importantly, it ensures collision-free operations even under localization
uncertainties. By integrating the improved Remote ID messages and uNMAC-based
RVO, we offer a solution to significantly increase airspace capacity while
adhering to strict safety standards. Our study emphasizes the potential to
augment the safety and efficiency of future drone operations, thereby
benefiting industries reliant on drone technologies.
