Mobile Robot DRL Navigation

A ROS2 framework for DRL autonomous navigation on mobile robots with LiDAR.

Table of contents

• Installation
  • Manual Installation
• Training and Testing
  • Command
  • Trained Models
  • Worlds
    • BARN Dataset
    • Other Worlds

Manual Installation

Docker Installation and detailed intallation guide can refer to the original repository.

Dependencies

• Ubuntu 20.04 LTS (Focal Fossa) download
• ROS2 Foxy Fitzroy
• Gazebo (Version 11.0)
• PyTorch (Version: 1.10.0)

Downloading the code base and building

First, make sure you have the turtlebot3-description package by running:

sudo apt-get install ros-foxy-turtlebot3-description

Open a terminal in the desired location for the new workspace. Clone the repository using:

git clone https://github.com/tomasvr/turtlebot3_drlnav.git

cd into the directory and make sure you are on the main branch

cd turtlebot3_drlnav
git checkout main

Next, install the correct rosdep tool

sudo apt install python3-rosdep2

Then initialize rosdep by running

rosdep update

Now we can use rosdep to install all ROS packages needed by our repository

rosdep install -i --from-path src --rosdistro foxy -y

Now that we have all of the packages in place it is time to build the repository. First update your package list

sudo apt update

Then install the build tool colcon which we will use to build our ROS2 package

sudo apt install python3-colcon-common-extensions

Next, it's time to actually build the repository code!

colcon build

After colcon has finished building source the repository

source install/setup.bash

The last thing we need to do before running the code is add a few lines to our ~/.bashrc file so that they are automatically executed whenever we open a new terminal. Add the following lines at the end of your ~/.bashrc file and replace ~/path/to/turtlebot3_drlnav/repo with the path where you cloned the repository. (e.g. ~/turtlebot3_drlnav)

# ROS2 domain id for network communication, machines with the same ID will receive each others' messages
export ROS_DOMAIN_ID=1

# Fill in the path to where you cloned the turtlebot3_drlnav repo
WORKSPACE_DIR=~/path/to/turtlebot3_drlnav
export DRLNAV_BASE_PATH=$WORKSPACE_DIR

# Source the workspace
source $WORKSPACE_DIR/install/setup.bash

# Allow gazebo to find our turtlebot3 models
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:$WORKSPACE_DIR/src/turtlebot3_simulations/turtlebot3_gazebo/models

# Select which turtlebot3 model we will be using (default: burger, waffle, waffle_pi)
export TURTLEBOT3_MODEL=burger

# Allow Gazebo to find the plugin for moving the obstacles
export GAZEBO_PLUGIN_PATH=$GAZEBO_PLUGIN_PATH:$WORKSPACE_DIR/src/turtlebot3_simulations/turtlebot3_gazebo/models/turtlebot3_drl_world/obstacle_plugin/lib

For more detailed instructions on ros workspaces check this guide.

Note: Always make sure to first run source install/setup.bash or open a fresh terminal after building with colcon build.

Training and Testing

Commands

Open up four different terminals however you like (I recommended using terminator or tmux for multi-tab). In the first terminal run

ros2 launch turtlebot3_gazebo turtlebot3_drl_stage4.launch.py

In a second terminal run

ros2 run turtlebot3_drl gazebo_goals

In a third terminal run

ros2 run turtlebot3_drl environment

In the fourth terminal run the ddpg agent

# train
ros2 run turtlebot3_drl train_agent ddpg "examples/ddpg_0_stage9" 8000
# test
ros2 run turtlebot3_drl test_agent ddpg "examples/ddpg_0_stage9" 8000

Trained Models

• DQN: 2900 episodes
• DDPG: 8000 episodes
• TD3: 7400 episodes

Worlds

In turtlebot3_drl_stage12.launch, you can use the world_file_name argument to change the simulation world.

ros2 launch turtlebot3_gazebo turtlebot3_drl_stage12.launch.py world_file_name:=wall1.model

BARN Dataset

Testing TD3 in BARN dataset

Other Worlds

• apartment.world
• wall1.world