Remote ID for separation provision and multi-agent navigation

TL;DR

This paper presents an enhanced collision avoidance method for multi-drone operations that integrates improved Remote ID data with a new collision definition, significantly increasing safety and efficiency.

Contribution

It introduces UAV NMAC and modifies Remote ID to include localization accuracy and drone size, enabling better collision avoidance under uncertainties.

Findings

Halves mission execution times compared to standard Remote ID-based RVO.

Ensures collision-free operation despite localization uncertainties.

Increases airspace capacity while maintaining safety standards.

Abstract

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.