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FlightEvo

Background

The code in this repository was used for winning the ICRA 2022 DodgeDrone Challenge in the vision-based category. It uses a fork of Agile Flight for the training environment and depends on a fork of NEAT Python and a fork of PyTorch NEAT.

Installation

Usage

  • First launch ROS by running roslaunch cfg/tools.launch in a terminal.
  • Next:
    • either, for training, run python -m flightevo.dodge_trainer in a separate terminal,
    • or, for evalutation, run python -m flightevo.dodge_evaluator in a separate terminal.

You can have a look at the arguments of these modules to play around with different settings.

Analysis

For an analysis of the methodology and final results, please look in the analysis folder.

Acknowledgements

Next to the contributors of the original repositories that were used, this repository is created in collaboration with guidoAI and NPU-yuhang, and the MAVLab organization.

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