Pushing the Limits: High-Speed UAV Experimentation and Deep Telemetry with AERPAW

Keiwan Soltani

Doctoral Researcher, Missouri University of S&T

Keiwan Soltani, a doctoral researcher at the University of Science and Technology, is exploring how UAV systems perform under extreme operational conditions, where both hardware limits and data fidelity become critical. A key challenge in this space is the lack of platforms that support sustained, high-speed flight testing while also providing access to detailed, low-level telemetry needed for advanced analysis. Traditional UAV testing environments often limit either the scale of real-world experimentation or the depth of system-level data available to researchers.

To address this, Soltani designed a series of four interrelated experiments aimed at pushing UAVs to their performance limits, specifically targeting sustained top-speed flight regimes of up to 20 m/s. His approach emphasized iterative experimentation, where each experiment informed the next, enabling continuous refinement of both system behavior and data collection strategies. Leveraging AERPAW’s flexible experimentation framework, Soltani independently designed, submitted, and refined his experiments within the virtual environment before executing them on the physical testbed.

AERPAW played a critical role in enabling this work by providing access to a large-scale, 7 square kilometer outdoor testbed where high-speed flights could be conducted safely and reliably. All four experiments were executed successfully on the first attempt, demonstrating both the robustness of the platform and the effectiveness of the preparation workflow. Equally important, AERPAW’s open and researcher-friendly design provided access to custom, low-level telemetry—including ESC and dataflash logs—allowing Soltani to analyze UAV performance at a level of detail rarely available in experimental platforms. Supported by hands-on engagement from the AERPAW team, this capability enabled deeper insights into system behavior under stress conditions. Through this work, Soltani demonstrates how integrated, high-fidelity experimentation environments can accelerate advanced UAV research, enabling rigorous validation of performance, reliability, and data integrity in real-world conditions.

His research contributes to ongoing work in UAV-enabled data collection, including “UAVDCH: Multi-Channel UAV Data Collection via Dual Cluster Heads in IoT Sensor Networks,” currently under review in IEEE Transactions on Emerging Topics in Computing.