Real-time Planning of Minimum-time Trajectories for Agile UAV Flight

This repository contains the code for Real-time Planning of Minimum-time Trajectories for Agile UAV Flight presented in our paper.

Citing

If you use this code in an academic context, please cite the following publication:

K. Teissing, M. Novosad, R. Penicka and M. Saska, "Real-time Planning of Minimum-time Trajectories for Agile UAV Flight," in IEEE Robotics and Automation Letters, 2024. (PDF)

@ARTICLE{teissing2024pmm,
  author={Teissing, Krystof and Novosad, Matej and Penicka, Robert and Saska, Martin},
  journal={IEEE Robotics and Automation Letters}, 
  title={Real-time Planning of Minimum-time Trajectories for Agile UAV Flight}, 
  year={2024},
  volume={},
  number={},
  pages={},
  doi={10.1109/LRA.2024.3471388}
}

License

GPL-3.0 License. Copyright (C) 2024 K. Teissing, M. Novosad, R. Pěnička, M. Saska (Faculty of Electrical Engineering, Czech Technical University in Prague).

This is a research code, expect that it changes often and any fitness for a particular purpose is disclaimed.

Installation

The code has been developed and tested on Ubuntu 20.04.

Downloading the code

Clone the repository

git clone [email protected]:ctu-mrs/pmm_uav_planner.git
cd pmm_uav_planner/

Compile the code

make

Usage Demonstration

main.cpp contains a demonstration on how to use our planner. In the demo, the paramaters of our planner are loaded from the planner_config.yaml file and the waypoints, through which the trajectory is planned, are loaded from the waypoints_config.yaml file. A sample planner config file with recommended parameters can be found in config/planner. For more information about parameters in planner_config.yaml have a look at our paper! The paths presented in our paper are located in config/waypoints. The call structure is ./main path/to/planner_config_file path/to/waypoints_config_file.

For example, to generate a trajectory for the race map, simply run:

./main config/planner/planner_config.yaml config/waypoints/race.yaml

Trajectory Visualization

A python script is used to visualize the optimized trajectory. We recommend creating a virtual environment and activating it using:

python3 -m venv env
source env/bin/activate

Afterwards install the python dependencies using pip (or pip3 if python3 is not default):

pip install numpy matplotlib

We have prepared two scripts for visualization of resulting trajectory. Note that parameter export::sampled_trajectory has to be set to true in planner_config.yaml, for visualization to work. The sampled trajectory will then be saved in trajectory_data/ as a csv file (without header) in the format

t, p_x, p_y, p_z, v_x, v_y, v_z, a_x, a_y, a_z

where t [s] contains timestamps, (p_x, p_y, p_z) [m] is the position vector, (v_x, v_y, v_z) [m/s] is the velocity vector, and (a_x, a_y, a_z) [m/s^2] is the acceleration vector.

To visualize a 3D view of the trajectory run:

python3 scripts/plot_trajectory.py scripts/trajectory_data/sampled_trajectory.csv

To visualize position, velocity and acceleration profiles of the trajectory run:

python3 scripts/plot_profiles.py scripts/trajectory_data/sampled_trajectory.csv

You can replace sampled_trajectory.csv with the file containing sampled trajectory you want to visualize.