UB-ANC Emulator

Introduction

UB-ANC Emulator is an open-source software environment for designing, implementing, testing, and debugging missions involving one or more networked drones before moving to field experimentation.

It combines mission-planning logic, MAVLink-compatible flight control, software-in-the-loop simulation, sensing, data logging, ground-control visualization, wireless-network modeling, and optional integration with physical communication hardware.

The framework is designed to help researchers study communications, networking, robotics, autonomy, and multi-agent coordination as parts of one integrated cyber-physical system.

UB-ANC Drone in the Software-Defined Radio Configuration

The physical UB-ANC platform combines autonomous flight control, onboard computing, mission-control software, and a reconfigurable software-defined radio for airborne networking and communication experiments.

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Figure source J. Modares and N. Mastronarde, “UB-ANC Drone: A Flexible Airborne Networking and Communications Testbed,” Figure 1.
View publication

Publication attribution retained

Key Features

How the Emulator Works

The following sequence summarizes the normal execution flow from startup to mission logging.

Physical UB-ANC Testbed

The physical UB-ANC platform combines an autonomous vehicle, an embedded computer, a MAVLink-compatible flight controller, and a configurable wireless communication subsystem.

Its modular architecture separates mission logic from the underlying network and flight-controller technologies. This makes it possible to evaluate the same application using Wi-Fi, Zigbee, LTE, GNU Radio, or software-defined radio hardware.

UB-ANC Testbed Software and Communication Architecture

The Agent Control Unit coordinates mission behavior through the Network Control Unit, MAVLink Control Unit, and Logging Unit. The communication interface can connect to GNU Radio and SDR hardware or to a conventional wireless network.

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Figure source J. Modares and N. Mastronarde, “UB-ANC Drone: A Flexible Airborne Networking and Communications Testbed,” Figure 2.
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Publication attribution retained

Experimental Drone Platform

The experimental platform used in the emulator-validation studies included a custom multirotor frame, a Pixhawk flight controller, a Raspberry Pi onboard computer, power sensing, and a high-capacity battery.

Custom UB-ANC Experimental Drone

Custom-built multirotor used for UB-ANC validation experiments, including the Pixhawk flight controller, Raspberry Pi onboard computer, sensing hardware, and battery system.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Simulating Unmanned Aerial Vehicle Swarms with the UB-ANC Emulator,” Figure 2.
View publication

CC BY-NC 4.0 article

UB-ANC Emulator Architecture

The emulator is organized around three central elements: the Emulation Engine, one MAV Object for each vehicle, and the UB-ANC Agent that implements mission behavior.

UB-ANC Emulator Software Architecture

The Emulation Engine manages multiple MAV Objects. Each MAV Object contains the UB-ANC Agent, object attributes, network and sensor interfaces, logging, MAVLink control, and a software-in- the-loop flight-controller simulator.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Simulating Unmanned Aerial Vehicle Swarms with the UB-ANC Emulator,” Figure 1.
View publication

CC BY-NC 4.0 article

UB-ANC Agent Model

Flight and Network Models

The default emulator prioritizes scalability. For network studies that require realistic packet-level behavior, UB-ANC can integrate with ns-3.

UB-ANC supplies agent behavior, mobility, and packet requests, while ns-3 models application, transport, network, link, physical, channel, mobility, and routing behavior.

ns-3 Integration with UB-ANC

Packet-transmission events, packet-delivery events, and vehicle position updates are exchanged between UB-ANC MAV Objects and their corresponding ns-3 nodes.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Realistic Network Simulation in the UB-ANC Aerial Vehicle Network Emulator,” Figure 2.
View publication

IEEE publication — check reuse terms

Network-Simulator Interface

External Hardware Integration

UB-ANC supports progressive and mixed emulation. A researcher can emulate flight and mission behavior while routing communication through physical radio hardware.

USRP N210 Hardware-in-the-Loop Integration

Two emulated MAVs communicate through physical USRP N210 software-defined radios connected through an Ethernet switch, while their flight and mission behavior remain inside the emulator.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Simulating Unmanned Aerial Vehicle Swarms with the UB-ANC Emulator,” Figure 6.
View publication

CC BY-NC 4.0 article

Software Prerequisites

The original installation instructions recommend Ubuntu 16.04. The documented emulator version uses QGroundControl 3.2 and ns-3.27.

sudo apt-get update && sudo apt-get upgrade

sudo apt-get install build-essential \
    libgl1-mesa-dev \
    libsdl2-dev \
    python-future \
    libfontconfig1 \
    dbus-x11 \
    geoclue \
    curl \
    git \
    netcat \
    xvfb

Build

The project provides a build script that downloads, compiles, and configures the original emulator environment.

cd ~

mkdir ub-anc
cd ub-anc

curl -sSL \
    https://raw.githubusercontent.com/jmodares/UB-ANC-Emulator/master/script/build_emulator.sh \
    | bash

After installation, the emulator should be available at:

~/ub-anc/emulator/

Add the current user to the dialout group:

sudo usermod -a -G dialout $USER

Configuration: Objects Directory

The objects directory defines the emulated agents. Each subdirectory represents one vehicle and follows this format:

mav_001
mav_002
mav_003
...
mav_250

To create ten agents using the default object template:

cd ~/ub-anc/emulator

./setup_objects.sh 10

This creates mav_001 through mav_010.

Custom Missions

The default example uses the follower mission . New missions can be created from the UB-ANC Agent template .

1. Implement the custom mission behavior.
2. Compile the mission as an executable named agent.
3. Copy it into the emulator's object template directory.
4. Run setup_objects.sh to create the required number of agents.
5. Start the emulator and monitor the mission.

Run the Emulator

cd ~/ub-anc/emulator

./start_emulator.sh

The startup script launches the flight-controller instances, establishes emulator connections, and then starts each mission agent.

Ground-Control Visualization

APM Planner displays the search area, active vehicles, traveled paths, selected vehicle status, command controls, and MAVLink messages during an emulated multi-drone mission.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Simulating Unmanned Aerial Vehicle Swarms with the UB-ANC Emulator,” Figure 3.
View publication

CC BY-NC 4.0 article

Wait until the following messages appear before beginning a mission:

[XXX] Info: EKF2 IMU0 is using GPS
[XXX] Info: EKF2 IMU1 is using GPS

Emulation and Experimental Validation

UB-ANC Emulator was evaluated by comparing software emulation with real drone experiments.

What Was Evaluated

• A coordinated three-drone takeoff, loiter, communication, and landing mission.
• Arming, landing, disarming, longitude, altitude, and mission timing.
• Leader-follower scenarios with up to 100 emulated MAVs in the reported test environment.
• Communication through physical USRP N210 radios.
• Motor current, energy use, flight speed, and barometric pressure.
• Link-level and end-to-end networking behavior using ns-3.

Emulation versus Real Three-Drone Experiments

Comparison of mission-event timing, longitude deviation, and relative altitude for a coordinated mission executed in both the emulator and the physical UB-ANC testbed.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Simulating Unmanned Aerial Vehicle Swarms with the UB-ANC Emulator,” Figure 4.
View publication

CC BY-NC 4.0 article

Research Applications

Energy-Efficient Coverage Path Planning

UB-ANC has been used to study coverage-path planning for single and multiple drones. The associated research models energy consumption based on traveled distance and turning behavior.

Planning methods were evaluated through optimization, emulation, and physical drone experiments with virtual obstacles.

Energy-Aware Coverage Path Planning

Planned and experimentally executed coverage paths around virtual obstacles, comparing a depth-limited search method with the energy-aware LKH-D approach.

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Figure source J. Modares, N. Mastronarde, and K. Dantu, “Simulating Unmanned Aerial Vehicle Swarms with the UB-ANC Emulator,” Figure 8.
View publication

CC BY-NC 4.0 article

Limitations

• The default flight model prioritizes scalability rather than full high-fidelity aerodynamic simulation.
• The basic network model is range based. Realistic packet-level networking requires ns-3 or external communication hardware.
• Timing differences can occur between emulation and physical flight, especially around takeoff and other flight transitions.
• The original software dependencies are old and may not build directly on current Linux distributions.
• The reported 100-agent scalability result was obtained using a specific historical computer and leader-follower scenario; it is not a universal maximum.
• Successful emulation does not replace physical validation, flight safety reviews, trained operators, or compliance with applicable aviation regulations.

Advanced Options

QGroundControl, flight-controller, and ns-3 parameters can be passed or changed by editing the start_emulator function inside start_emulator.sh.

Console Mode

cd ~/ub-anc/emulator

./start_emulator.sh -c

The documented console-mode configuration uses AODV routing by default. Routing, propagation, gain, data rate, and logging settings can be adjusted in the startup script or associated ns-3 code.

Output Traces and Logging

The default startup script suppresses agent output by redirecting it to /dev/null. This reduces overhead when running many agents.

For debugging or experimental analysis, modify the startup script to display logs in real time or save them to files.

Docker

Docker provides the most practical way to reproduce the historical UB-ANC software environment.

Build the Image

docker build -t jmod/ub-anc-emulator:latest \
    https://raw.githubusercontent.com/jmodares/UB-ANC-Emulator/master/script/Dockerfile

Run with Linux GUI Support

xauth nlist $DISPLAY \
    | sed -e 's/^..../ffff/' \
    | xauth -f /tmp/.docker.xauth nmerge -

docker run -it \
    --env DISPLAY=$DISPLAY \
    --env XAUTHORITY=/tmp/.docker.xauth \
    --volume /tmp/.X11-unix:/tmp/.X11-unix \
    --volume /tmp/.docker.xauth:/tmp/.docker.xauth \
    --device /dev/dri:/dev/dri \
    --device /dev/snd:/dev/snd \
    --volume $PWD/docker:/tmp/emulator \
    --publish 5770:5770 \
    --publish 5780:5780 \
    jmod/ub-anc-emulator:latest

Run without GUI Mapping

docker run -it \
    --volume $PWD/docker:/tmp/emulator \
    --publish 5770:5770 \
    --publish 5780:5780 \
    jmod/ub-anc-emulator:latest

Inside the container:

cp -r emulator/ /tmp/

cd /tmp/emulator

Port Mapping

Ground-control applications connect to agent i using:

port = 10 * i + 5760

Source Code

• Current repository: github.com/nmastron/UB-ANC-Emulator
• Original repository: github.com/jmodares/UB-ANC-Emulator
• Mission template: UB-ANC Agent
• Follower mission: follower

Repository Structure

UB-ANC-Emulator/
├── engine/                 # Core emulator components
├── qgc_gui/                # Ground-control GUI integration
├── script/                 # Build, setup, startup, and Docker scripts
├── README.md               # Project documentation
├── UB-ANC-Emulator.pro     # Qt project configuration
└── .gitignore

Where to Start in the Code

UB-ANC Research Papers

Citation

Cite the main UB-ANC Emulator publication:

@inproceedings{modares2016ubanc,
  title     = {{UB-ANC Emulator}: An Emulation Framework for
               Multi-Agent Drone Networks},
  author    = {Jalil Modares and Nicholas Mastronarde and
               Karthik Dantu},
  booktitle = {Proceedings of the IEEE International Conference
               on Simulation, Modeling, and Programming for
               Autonomous Robots},
  year      = {2016},
  doi       = {10.1109/SIMPAR.2016.7862404}
}

Additional Publications

@article{modares2019simulating,
  title   = {Simulating Unmanned Aerial Vehicle Swarms with
             the UB-ANC Emulator},
  author  = {Jalil Modares and Nicholas Mastronarde and
             Karthik Dantu},
  journal = {International Journal of Micro Air Vehicles},
  year    = {2019},
  doi     = {10.1177/1756829319837668}
}

@inproceedings{modares2017realistic,
  title     = {Realistic Network Simulation in the UB-ANC
               Aerial Vehicle Network Emulator},
  author    = {Jalil Modares and Nicholas Mastronarde and
               Karthik Dantu},
  booktitle = {IEEE INFOCOM Workshops},
  year      = {2017},
  doi       = {10.1109/INFCOMW.2017.8116354}
}

@inproceedings{modares2017planner,
  title     = {{UB-ANC Planner}: Energy Efficient Coverage
               Path Planning with Multiple Drones},
  author    = {Jalil Modares and Farshad Ghanei and
               Nicholas Mastronarde and Karthik Dantu},
  booktitle = {IEEE International Conference on Robotics
               and Automation},
  year      = {2017},
  doi       = {10.1109/ICRA.2017.7989732}
}

@misc{modares2015ubanc,
  title  = {{UB-ANC Drone}: A Flexible Airborne Networking
            and Communications Testbed},
  author = {Jalil Modares and Nicholas Mastronarde},
  year   = {2015},
  note   = {arXiv:1509.08346}
}

License and Figure Attribution

Software License

UB-ANC Emulator is distributed under the GNU General Public License, version 3 or later. Refer to the repository license and source-code headers for the complete software terms.

Research Figures

The images displayed on this page originate from UB-ANC research publications and include an attribution block beneath every figure. The UB-ANC software license does not automatically apply to figures published in research papers.