# Autonomously Following Forest Paths with a Mobile Robot using Semantic Segmentation

**Website:** [pathy.pfiers.net](https://pathy.pfiers.net)

This project is [Pieter Fiers](https://pfiers.net) and
 [Simon Germeau](https://github.com/GermeauSimon)'s professional bachelor's thesis for [UCLL](https://www.ucll.be/)'s [Aplied Informatics degree](http://onderwijsaanbod.leuven.ucll.be/opleidingen/n/SC_52335187.htm).

We created a rover that is capable of autonomously following forest paths. 

We used a Semantic Segmentation CNN, as opposed to the classification CNN 
used by Giusti et al.[^1] and Smolyanskiy et al.[^2]. We believe this enables 
interesting future expansions, like higher-level decision making about path 
intersections, and the mapping of road geometries.

<video width="100%" controls>
    <source src="intro.webm" type="video/webm">
</video>

## Repository structure

The repository is structured according to the four main stages of our project:

[Dataprep](dataprep/README.md) - Documentation about, and the scripts we used for, the processing and labelling 
of data used for training.

[Model](model/README.md) - This folder contains the machine learning process we used to train our CNN.

[Rover](rover/README.md) - Documentation regarding the hardware aspect of our rover.

[ROS](ros/README.md) - All documentation, ROS ([Robot Operating System](https://www.ros.org/)) nodes, and extra files needed to make the rover drive
itself.

## Presentation

[As pdf](presentation.pdf) or [as pptx](presentation.pptx) &nbsp; <small>[source](https://docs.google.com/presentation/d/1WMvPDMJ7YyHw0fyk2sWPqvgLIdivagkhP7UBCjnO4ww/edit?usp=sharing)</small>

## Demo videos

All demo videos combined (see YouTube description for timestamps):

<iframe width="560" height="315" src="https://www.youtube.com/embed/SpPcj6MqQEs" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[youtu.be/SpPcj6MqQEs](https://youtu.be/SpPcj6MqQEs)

![Collage of the pathy rover and prediction for a forest path](pathy-collage.webp)

<br>

[^1]: Smolyanskiy et al., “Toward Low-Flying Autonomous MAV Trail Navigation using DeepNeural Networks for Environmental Awareness” May 2017.  
[^2]: Giusti et al., “A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots” Dec 2015. 

{include-in-output}`intro.webm`
{include-in-output}`presentation.pdf`
{include-in-output}`presentation.pptx`
{include-in-output}`documentation-as-pdf.png`