Autonomous navigation of a drone in indoor environments

This work is a research and development project on autonomous navigation algorithms for a small programmable drone in indoor environments free of dynamic obstacles. The hypothesis that the drone has access to a simple representation of its environment, in order to plan paths and analyze them, was posed.

Context

Master's thesis carried out to obtain the degree of Master of Science in Computer Science Engineering (University of Liège, Faculty of Applied Science, academic year 2020-2021).

Main objective

The main objective of this work was to explore and test modern techniques allowing the autonomous navigation of a drone in an indoor environment.

The main techniques explored are:

• the calculation of vanishing point to adjust the drone;
• the use of neural networks for various tasks (associating an action to an image, computing vanishing point, depth estimation);
• the use of markers (ArUco, QR code) to guide the drone.

Tests were first performed on a simulator (Unreal Engine 4 with the AirSim plugin) and then in the corridors of a building with a DJI Tello EDU.

Resources

Data

The configuration of the AirSim plugin as well as the simulated environments created on Unreal Engine 4 are described and available here.

The data sets constituted on each of the simulated environments are described and available here.

Examples of environment representations, used by the algorithms, are available here.

Code

The bash/ folder contains miscellaneous scripts used mainly to process images.

The main implementation of the algorithms is located in the python/ folder. More precisely,

• airsim/ is the AirSim Python package;
• analysis/ is a module containing implementation of marker detection and decoding algorithms and vanishing point detection algorithms;
• learning/ is a module containing implementation of all elements related to Deep Learning (data sets, models, training and testing procedures);
• misc/ contains miscellaneous scripts, mainly tests used to display and evaluate intermediate results;
• plots/ is a module containing settings to create LaTeX plots with matplotlib package;
• uav/ is the main module that contains implementation of the controllers, environnement representation and all navigation modules and algorithms.

Files learn.py and navigate.py are main files used to, respectively, train and evaluate a neural network and navigate the drone.

Results

All results obtained are explicited in my thesis report.

Important results are illustrated by videos available on this YouTube playlist.

Try it yourself

First, make sure to create and activate the Anaconda environment using

conda env create -f environment.yml
conda activate autonomous-drone

Then, you can simply run the navigate.py script using, for example,

python navigate.py --environment my_env.txt --controller airsim --algorithm vision --show

Help with arguments can be obtained via

python navigate.py --help

Custom environment

If you want to work with your (simulated or real) environment, make sure to create a representation as described in the Environment class of the environment.py file. Examples can be found here.

Deep Learning

If you want to use navigation algorithms that use neural network(s), make sure to train model(s) using the learn.py script.

Help with arguments can be obtained via

python learn.py --help

Exemples of ready-to-use data sets are available here.

References

All references used are listed in my thesis report.