Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments
🤖 AI Summary
This work addresses the challenge of efficient multi-base-station data collection by autonomous unmanned aerial vehicles (UAVs) in dynamic wireless environments through the organization of the AERPAW Autonomous Aerial Data Mule (AADM) Challenge. Participants were tasked with designing flight control and decision-making algorithms to maximize data download efficiency. The study employs a two-stage framework combining digital twin simulation and outdoor field trials, and presents the first multimodal UAV wireless dataset integrating USRP, LoRa, Keysight RF sensors, Fortem radar, and UAV telemetry systems. The resulting dataset encompasses multivariate information including link quality, downloaded data volume, position estimates, and radar perception, establishing a reproducible open benchmark that supports cutting-edge research in autonomous networking, cross-domain transfer learning, and integrated sensing and communication.
Application Category
📝 Abstract
In this work, we present an unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project. The AADM challenge was the second competition in which an autonomous UAV acted as a data mule, where the UAV downloaded data from multiple base stations (BSs) in a dynamic wireless environment. Participating teams designed flight control and decision-making algorithms for choosing which BSs to communicate with and how to plan flight trajectories to maximize data download within a mission completion time. The competition was conducted in two stages: Stage 1 involved development and experimentation using a digital twin (DT) environment, and in Stage 2, the final test run was conducted on the outdoor testbed. The total score for each team was compiled from both stages. The resulting dataset includes link quality and data download measurements, both in DT and physical environments. Along with the USRP measurements used in the contest, the dataset also includes UAV telemetry, Keysight RF sensors position estimates, link quality measurements from LoRa receivers, and Fortem radar measurements. It supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication, which serves as a benchmark for future autonomous wireless experimentation.
Problem
Research questions and friction points this paper is trying to address.
UAV
data mule
multi-cell association
dynamic wireless environment
mission time constraint
Innovation
Methods, ideas, or system contributions that make the work stand out.
UAV data mule
digital twin
multi-modal wireless measurements
autonomous aerial networking
DT-to-real-world transfer learning
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