A UAV-assisted Wireless Localization Challenge on AERPAW

TL;DR

The paper presents the AFAR challenge on the AERPAW platform, a large-scale UAV-enabled wireless localization testbed that combines digital twin emulation with live experiments. It documents the platform architecture, competition setup, and approaches from three top teams (NYU, UNT, UGA) across simulation and field tests, highlighting the challenges of noisy, narrowband localization and propagation mismatch. The results reveal substantial gaps between digital twin and real-world wireless propagation, while also demonstrating the platform’s value for rapid, collaborative experimentation and method refinement. Overall, the work showcases AERPAW as a practical tool for accelerating wireless research in aerial robotics and outlines steps to improve emulator fidelity and cross-domain validation.

Abstract

As wireless researchers are tasked to enable wireless communication as infrastructure in more dynamic aerial settings, there is a growing need for large-scale experimental platforms that provide realistic, reproducible, and reliable experimental validation. To bridge the research-to-implementation gap, the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) offers open-source tools, reference experiments, and hardware to facilitate and evaluate the development of wireless research in controlled digital twin environments and live testbed flights. The inaugural AERPAW Challenge, "Find a Rover," was issued to spark collaborative efforts and test the platform's capabilities. The task involved localizing a narrowband wireless signal, with teams given ten minutes to find the "rover" within a twenty-acre area. By engaging in this exercise, researchers can validate the platform's value as a tool for innovation in wireless communications research within aerial robotics. This paper recounts the methods and experiences of the top three teams in automating and rapidly locating a wireless signal by automating and controlling an aerial drone in a realistic testbed scenario.

Figures (5)

• Figure 1: Physical setup of the AFAR competition, with the blue area showing the allowable locations for the UAV, the green area the hiding area of the rover, and the three markers showing the three hiding spots during the competition.
• Figure 2: Error of location estimate of the NYU team solution, and the baseline solution provided by AERPAW. The dashed vertical line marks the three-minute mark, at which the fast estimate is due.
• Figure 3: UNTs recursive algorithm final result shown in QGroundControl, the open-source flight control and mission planner program used by AERPAW.
• Figure 4: Shows the progressive approach for creating a radio map estimate. (a) is a final simulation using Robotarium. (b) is a digital twin emulation test using AERPAW, and (c) is a reconstruction created using flight data logs from the second trial on the AERPAW testbed.
• Figure 5: Snapshot taken during the competition with the UAV searching for the rover at Location 3.