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Propagation Data from AERPAW Find-a-Rover (AFAR) Challenge in December 2023

2024
2023, 3.3 GHz, Drone, Localization, Propagation Measurements, Rural, Student Challenge Dataset, Trajectory, UGV
Canonical Experiments, GE2: Channel Sounder, GNU Radio
Localization, UAV, UGV

Lead Experimenter

Ozgur Ozdemir, North Carolina State University

Link to Dataset

  • Dataset Link: The dataset includes power spectrum measurements and GPS logs that are collected by a drone in an urban environment. There are a total of 14 different datasets that are from field experiments carried out by 5 finalists at the AFAR challenge. There were three different unmanned ground vehicle (UGV) locations and the drone followed different trajectories for localizing the UGV. The SigMF format dataset including format conversion codes can be accessed here [Dryad].

Equipment and Software Used

USRB B205, GNU Radio, UAV and UGV

Description

In December 2023, the AERPAW Find-a-Rover (AFAR) Challenge hosted by the AERPAW platform finalized its field testing.

Objective of the Challenge

The primary objective of the AFAR Challenge was to demonstrate the capability of UAVs in accurately and swiftly localizing a UGV. Competitors were tasked with utilizing a UAV equipped with a software-defined radio (SDR) to detect and localize the UGV. The SDR on the UAV was designed to continuously receive a specific channel-sounding waveform, as detailed in the GE2 example experiment from the AERPAW user manual.

Technical Specifications and Constraints

Waveform Characteristics: The challenge mandated the use of a narrowband waveform with a bandwidth of 125 KHz. Competitors were restricted from altering the waveform parameters at the UGV, ensuring a standardized test environment.
Antenna Configuration: The system setup included one transmit antenna and one receiver antenna, with the antenna patterns for both being provided to the participants.
Environmental Data: Competitors were also given a geographical map of the environment to aid in the strategic deployment of the UAV.
Challenge Execution

Participants in the challenge had the flexibility to either use fixed waypoints for the UAV or develop their own algorithms for trajectory updates. These algorithms could instruct the UAV on the next waypoint to fly to, based on the observed signal strength received from the UGV.

While the experiments have been executed and the data has been collected by the AERPAW Operations team in the real-world testbed environment, the experiments have been originally developed by the participating teams in AERPAW's digital twin. This public dataset includes data for all teams from both the development environment and the real-world environment. Names of the teams, their corresponding institutions, and the names of the team leads, are as follows.

1) Eagles, University of North Texas (Lead: Jaya Sravani Mandapaka)
2) NYU Wireless, NYU (Lead: Weijie Wang)
3) Team SunLab, University of Georgia (Lead: Paul Kudyba)
4) Team Wolfpack, NC State University (Lead: Cole Dickerson)
5) Daedalic Wings, NC State University (Lead: Baisakhi Chatterjee)

Publications

    Representative Results

    The results of measuring power versus operation time for all teams, during development at location 1, are provided below.

    The 3-D scatter plots representing the data for the testbed at location 1 for all teams are provided below.

     

    Potential use cases for this dataset include:

    • Search and Rescue Operations
    • Autonomous Vehicle Collaboration
    • Trajectory Optimization
    • Narrowband Propagation Modeling