DRL-robot-navigation

DRL-robot-navigation

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles. Obstacles are detected by laser readings and a goal is given to the robot in polar coordinates. Trained in ROS Gazebo simulator with PyTorch. Tested with ROS Noetic on Ubuntu 20.04 with python 3.8.10 and pytorch 1.10.

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Installation and code overview tutorial available here

Training example:

ICRA 2022 and IEEE RA-L paper:

Some more information about the implementation is available here

Please cite as:

@ARTICLE{9645287,
  author={Cimurs, Reinis and Suh, Il Hong and Lee, Jin Han},
  journal={IEEE Robotics and Automation Letters}, 
  title={Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning}, 
  year={2022},
  volume={7},
  number={2},
  pages={730-737},
  doi={10.1109/LRA.2021.3133591}}

Installation

Main dependencies:

• ROS Noetic
• PyTorch
• Tensorboard

Clone the repository:

$ cd ~
### Clone this repo
$ git clone https://github.com/reiniscimurs/DRL-robot-navigation

The network can be run with a standard 2D laser, but this implementation uses a simulated 3D Velodyne sensor

Compile the workspace:

$ cd ~/DRL-robot-navigation/catkin_ws
### Compile
$ catkin_make_isolated

Open a terminal and set up sources:

$ export ROS_HOSTNAME=localhost
$ export ROS_MASTER_URI=http://localhost:11311
$ export ROS_PORT_SIM=11311
$ export GAZEBO_RESOURCE_PATH=~/DRL-robot-navigation/catkin_ws/src/multi_robot_scenario/launch
$ source ~/.bashrc
$ cd ~/DRL-robot-navigation/catkin_ws
$ source devel_isolated/setup.bash

Run the training:

$ cd ~/DRL-robot-navigation/TD3
$ python3 train_velodyne_td3.py

To check the training process on tensorboard:

$ cd ~/DRL-robot-navigation/TD3
$ tensorboard --logdir runs

To kill the training process:

$ killall -9 rosout roslaunch rosmaster gzserver nodelet robot_state_publisher gzclient python python3

Once training is completed, test the model:

$ cd ~/DRL-robot-navigation/TD3
$ python3 test_velodyne_td3.py

Gazebo environment:

Rviz:

Running in Docker

It is also possible to run the training inside a docker container in headless mode (no rviz/gazebo).
Build the docker image:

$ sudo docker build -t drl_noetic .

Then run the docker image in interactive mode:

$ sudo docker run -it --rm drl_noetic

Inside the docker, start the training:

$ cd DRL-robot-navigation/TD3
$ python3 train_velodyne_td3.py