This is a fork of the original repository under the project Smart_POV which funds basics for intelligence development on simulated voyager agents. The original code is editted such that it simply interacts - and may be evaluated - with our developed professional (D)RL-evaluation platform MIIO2V. A comparison of this project with other similar works id documented here.
- Ubuntu 20.04
- ROS Noetic
- Anaconda
"Note: You may encounter some errors during running the simulation. Make sure the rospkg and defusedxml package is installed."
Install the following packages:
sudo apt-get install ros-noetic-map-server
sudo apt-get install ros-noetic-move-base
sudo apt-get install ros-noetic-navigation
pip install rospkg
pip install defusedxml- Create a ROS workspace.
source /opt/ros/noetic/setup.bash
mkdir -p ~/rl_nav/src
cd ~/rl_nav/
catkin_make- Clone these repositories inside src directory.
cd src
git clone https://github.com/spov-research/rl_nav.git
git clone -b noetic-devel https://github.com/ROBOTIS-GIT/turtlebot3_simulations.git
git clone -b noetic-devel https://github.com/ROBOTIS-GIT/turtlebot3_msgs.git
git clone -b noetic-devel https://github.com/ROBOTIS-GIT/turtlebot3.git- Make the ros package.
cd ..
catkin_make- Create a conda environment with python 3.7 and tensorflow 2.1. then activate it.
conda create -n rlnav python=3.7 tensorflow=2.1
activate rlnav- Install the following packages inside the conda environment.
pip install keras==2.3.1- Training Stage 1:
- Open a terminal inside the workspace. then run:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_stage_1.launchOpen another terminal in workspace then run:
source devel/setup.bash
roslaunch rl_nav navigation_123.launchOpen another terminal in workspace then run:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
chmod +x src/rl_nav/src/turtlebot3_dqn.py
roslaunch rl_nav turtlebot3_dqn_stage_1.launchOnce done, run next stage(end all terminals first)
- Training Stage 2: Teminal 1:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_stage_2.launchTeminal 2:
source devel/setup.bash
roslaunch rl_nav navigation_123.launchTeminal 3:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_dqn_stage_2.launchOnce done, run next stage(end all terminals first)
- Training Stage 3: Teminal 1:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_stage_3.launchTeminal 2:
source devel/setup.bash
roslaunch rl_nav navigation_123.launchTeminal 3:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_dqn_stage_3.launchOnce done, we can run a trained agent with a global planner: Teminal 1:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_stage_4.launchTeminal 2:
source devel/setup.bash
roslaunch rl_nav navigation_4.launchTeminal 3:
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch rl_nav turtlebot3_dqn_stage_4_run_global.launch