Remote ID Based UAV Collision Avoidance Optimization for Low-Altitude Airspace Safety

TL;DR

This paper presents a Remote ID-based framework for UAV collision avoidance that optimizes communication protocols using deep reinforcement learning, significantly reducing delays and enhancing safety in low-altitude airspace.

Contribution

It introduces a novel distributed collision avoidance framework combined with an adaptive protocol selection algorithm using multi-agent deep Q-networks.

Findings

Reduced average communication delay by 32% with the proposed method.

Demonstrated the effectiveness of Remote ID for real-time UAV collision avoidance.

Validated the framework through numerical simulations showing improved safety and efficiency.

Abstract

With the rapid development of unmanned aerial vehicles (UAVs), it is paramount to ensure safe and efficient operations in open airspaces. The remote identification (Remote ID) is deemed an effective real-time UAV monitoring system by the federal aviation administration, which holds potentials for enabling inter-UAV communications. This paper deeply investigates the application of Remote ID for UAV collision avoidance while minimizing communication delays. First, we propose a Remote ID based distributed multi-UAV collision avoidance (DMUCA) framework to support the collision detection, avoidance decision-making, and trajectory recovery. Next, the average transmission delays for Remote ID messages are analyzed, incorporating the packet reception mechanisms and packet loss due to interference. The optimization problem is formulated to minimize the long-term average communication delay, where UAVs can flexibly select the Remote ID protocol to enhance the collision avoidance performance. To tackle the problem, we design a multi-agent deep Q-network based adaptive communication configuration algorithm, allowing UAVs to autonomously learn the optimal protocol configurations in dynamic environments. Finally, numerical results verify the feasibility of the proposed DMUCA framework, and the proposed mechanism can reduce the average delay by 32% compared to the fixed protocol configuration.