It's a collection for mapless robot navigation using RGB image as visual input. It contains the test
environment and motion planners, aiming at realizing all the three levels of mapless navigation:
1. memorizing efficiently;
2. from memorizing to reasoning;
3. more powerful reasoning
The simulation experiment data is in the ./materials/record folder.
I built the environment for testing the algorithms.
- Diverse complexity.
- Gym-style Interface.
- Support ROS.
Stacked LSTM
network structure
tensorflow: 1.5.0
OS: Ubuntu 16.04
Python: 2.7
ROS: Kinetic
Gazebo: 7
tensorforce: https://github.com/tensorforce/tensorforce
sudo apt-get install ros-kinetic-gazebo-ros-pkgs ros-kinetic-gazebo-ros-control
sudo apt-get install ros-kinetic-turtlebot-*
sudo apt-get remove ros-kinetic-turtlebot-description
sudo apt-get install ros-kinetic-kobuki-description
# change to catkin_ws/src
git clone https://github.com/marooncn/navbot
cd ..
catkin_make
source ./devel/setup.bash
# you can change the configure in config.py
cd src/navbot/rl_nav/scripts
# run the proposed model for memorizing
python PPO.py
# run the proposed model for reasoning
python E2E_PPO_rnn.py
-
The default environment is maze1, you need to change maze_id in nav_gazebo.launch and config.py if you want change the environment.
-
maze1 and maze2 are speeded up 10 times to train, if you want speed up other environments, just change
<max_step_size>0.001</max_step_size> <real_time_factor>1</real_time_factor>
to
<max_step_size>0.01</max_step_size> <!-- <real_time_factor>1</real_time_factor> -->
in the environment file in worlds.
-
To reproduce the result, please change the related parameters in config.py according to config.txt.
-
PPO is not a deterministic policy gradient algorithm, the action at every timestep is sampled according to the distribution. It can be seen as "noise" and it's useful for explorations and generalizations. If you want to use the best strategy after the model is trained, just change 'deterministic = True' in config.py and the performance will be improved.
If your find the work is helpful in your research, please cite the following papers:
- Using RGB Image as Visual Input for Mapless Robot Navigation (IEEE ITSC 2019 , under review)
- Learning to Navigate in Indoor Environments: from Memorizing to Reasoning (IEEE ITSC 2019 , under review)
tensorforce(blog)
gym_gazebo
gazebo
roslaunch python API
turtlebot_description
kobuki_description
WorldModelsExperiments(official)
WorldModels(by Applied Data Science)