Path Planning and Trajectory Optimization for Drones

This project is a collection of Python scripts and Jupyter Notebooks for path planning and trajectory optimization for drones in 2D and 3D environments. The project includes implementations of A* and LT* algorithms for path planning and a pseudospectral method for trajectory optimization.

Installation

To use the project, you will need to install the following Python packages:

• numpy
• matplotlib
• scipy
• cloudpickle
• pyomo

You can install these packages using pip:

pip install numpy matplotlib scipy cloudpickle pyomo

You will also need to install Jupyter Notebook to run the notebook files. You can install Jupyter Notebook using pip:

pip install jupyter

Usage

The project includes the following files:

• ps_control.py: Contains the implementation of the pseudospectral method for trajectory optimization.
• run_traj_search.py: Contains examples of using A* and LT* algorithms for path planning in 2D and 3D environments.
• worlds.py: Contains the implementation of the Gridmap class for creating 2D and 3D environments.
• path_plan.py: Contains an example of using A* and LT* algorithms for path planning in a simple 2D environment.

To use the Python scripts, simply run the desired script with Python.

• run_traj_search.py: script contains examples of using A* and LT* algorithms for path planning in various 2D and 3D environments.
• path_plan.py: script contains an example of using A* and LT* algorithms for path planning in a simple 2D environment.
• pscontrol.py: script contains the implementation of the pseudospectral method for trajectory optimization.

There are several Jupyter Notebooks that were utillized for testing or results vizualization:

• traj_plan.ipynb: experimental notebook with simple implementation of trajectory planning
• psm_check.ipynb: experimental notebook for processing the solution
• worlds.ipynb: experimental notebook with worlds definition and generation
• get_results.ipynb: evaluation of trajectories, generation of pandas tables, and export to LaTeX tables
• print_plots.ipynb: vizualization of trajectories and saving to png

The worlds.py script contains the implementation of the Gridmap class for creating 2D and 3D environments. The generate_some_worlds function in this script can be used to generate various 2D and 3D environments.

Using the Trajectory Planning Container

To use the Trajectory Planning container, you will need to have Singularity installed on your system. You can download Singularity from the official website: https://docs.sylabs.io/guides/2.6/user-guide/quick_start.html

To run the container, you can use the following command:

singularity shell traj_container.sif

It has build in conda environment drone_traj, which after shelling the conteriner is activated using

source activate drone_traj.

You can then run your Python scripts inside the container.

If you need to access files on your drive from within the container, you can use the --bind option to mount a directory from your system into the container. For example, if you want to mount the /home/user/data directory from your system into the /data directory in the container, you can use the following command:

singularity shell --bind /home/user/data:/data traj_container.sif

This will mount the /home/user/data directory from your system into the /data directory in the container. You can then access the files in the /home/user/data directory from within the container.

MetaCentrum Computational Grid

Data acrquisition was performed on coputational grid creating bulk jobs using script run_jobs.sh that calls job.sh with specific parameters for environemnt and problem setup. Config files were generated with Python script gen_traj_params.py