CrowdNav_Sim2Real

This repository contains the sim2real procedure and code for our paper titled "Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph" in ICRA 2023. In sim2real, we adapted a people detector and SLAM from previous works, and transfered a simulated crowd navigation policy to a TurtleBot2i without any real-world training.
For more details, please refer to the project website and arXiv preprint. For experiment demonstrations, please refer to the youtube video.
This repo only serves as a reference point for the sim2real transfer of crowd navigation. Since there are lots of uncertainties in real-world experiments that may affect performance, we cannot guarantee that it is reproducible on your case.

System overview

Hardware

Host computer:
- CPU: Intel i7-9700 @ 3GHz
- GPU: Nvidia RTX 2080
- Memory: 32GB
Turtlebot2i:
- On-board computer: Nvidia Jetson Xavier
- Lidar: RP-Lidar A3
- Tracking Camera: Intel Realsense T-265
- Mobile base: Kobuki base

Software

The host computer and the turtlebot communicates through ROS by connecting to the same WiFi network.

Host computer:
- OS: Ubuntu 20.04
- Python version: 3.8.10
- Cuda version: 11.5
- ROS version: Noetic (our code WILL NOT WORK with lower versions of ROS on host computer)
Turtlebot2i:
- OS: Linux
- Python version: 3.8
- Cuda version: cuda is not needed unless you're running everything on board (i.e. no host computer)
- ROS version: Melodic

Setup

Turtlebot

Create a catkin workspace

mkdir ~/catkin_ws
cd catkin_ws
mkdir -p src
catkin_make
cd src

Install ROS packages into your workspace

cd ~/catkin_ws/src
# turtlebot2
git clone https://github.com/turtlebot/turtlebot.git
git clone https://github.com/turtlebot/turtlebot_msgs.git
git clone https://github.com/turtlebot/turtlebot_apps.git
git clone https://github.com/turtlebot/turtlebot_interactions.git

# kobuki
git clone https://github.com/yujinrobot/kobuki_msgs.git

# RP-Lidar
git clone https://github.com/Slamtec/rplidar_ros.git

cd ~/catkin_ws
catkin_make

Install realsense-ros following this link
- Skip this step if you're planning to use LiDAR for robot localization.

Host computer

Create a catkin workspace

mkdir ~/catkin_ws
cd catkin_ws
mkdir -p src
catkin_make
cd src

Install ROS packages into your workspace

cd ~/catkin_ws/src
# turtlebot2
git clone https://github.com/turtlebot/turtlebot.git
git clone https://github.com/turtlebot/turtlebot_msgs.git
git clone https://github.com/turtlebot/turtlebot_apps.git
git clone https://github.com/turtlebot/turtlebot_interactions.git

# kobuki
git clone https://github.com/yujinrobot/kobuki_msgs.git

# to use lidar for SLAM
git clone https://github.com/surfertas/turtlebot2_lidar.git
git clone https://github.com/SteveMacenski/slam_toolbox.git
cd slam_toolbox
git checkout noetic-devel
rosdep install -q -y -r --from-paths src --ignore-src
cd ..

# people detector
git clone https://github.com/VisualComputingInstitute/2D_lidar_person_detection.git

cd ~/catkin_ws
catkin_make

In catkin_ws/src/2D_lidar_person_detection/dr_spaam_ros/config/topics.yaml line 14, change /segway/scan_multi to /person_pts to remove static obstacles from the input scans of people detector
- Otherwise, the policy network may receive false positive detections because the DR-SPAAM is not very robust w.r.t. different hardware and environments.
Place findloc_bgrm.py into catkin_ws/src/2D_lidar_person_detection
Download the virtual environment from this link. Create an identical virtual environment in your computer.
Connect the robot and host computer to the same WiFi network. In tb2.bash, change ROS_MASTER to the IP address of the robot, change ROS_IP to the IP address of the host computer.
- Skip this step and all steps related to source tb2.bash if you're running everything on a robot's on-board computer.

Run the code

Training in simulation

Clone the crowd navigation repo
Modify the configurations.
1. Modify the configurations in crowd_nav/configs/config.py and arguments.py following instructions here
2. In crowd_nav/configs/config.py, set
  - action_space.kinematics = "unicycle" if your robot has a differential drive
    - Explanations on holonomic robot v.s. differential drive robot
  - adjust sim.circle_radius, sim.arena_size, and sim.human_num based on your real environment
After you change the configurations, run
```
python train.py 
```
The checkpoints and configuration files will be saved to the folder specified by output_dir in arguments.py.
Test the trained policy in simulation following instructions here, make sure the results are satisfactory (success rate is at least around 90%)

Testing in real world

Create a map of the real environment using SLAM:
a. [Turtlebot] Launch the mobile base:
```
source catkin_ws/devel/setup.bash
roslaunch turtlebot2i_bringup minimal.launch
```
If 'no data stream, is kobuki turned on?' shows up even if the base is fully charged, we recommend unplugging the USB wire of LiDAR and restarting the Turtlebot's onboard computer.

b. [Turtlebot] Plug in the USB wire of LiDAR, launch the LiDAR:
```
source catkin_ws/devel/setup.bash && sudo chmod 666 /dev/ttyUSB0 && sudo chmod 666 /dev/ttyUSB1 && sudo chmod 666 /dev/ttyUSB2 
roslaunch rplidar.launch
```
c. [Host computer] Launch SLAM and navigation
```
source ~/tb2.bash
source ~/catkin_ws/devel/setup.bash
roslaunch turtlebot_navigation laser_gmapping_demo.launch 
```
d. [Host computer] Launch rviz
```
source ~/tb2.bash
source ~/catkin_ws/devel/setup.bash
roslaunch turbot_rviz nav.launch
```
e. [Host computer] Launch robot teleoperation
```
source ~/tb2.bash 
roslaunch turtlebot_teleop keyboard_teleop.launch
```
f. [Host computer] Teleoperate the robot around the environment until you are satisfied with the map in rviz, save the map by
```
rosrun map_server map_saver -f ~/map
```
In your home directory, you will see two files: map.yaml and map.pgm.
Then, test the trained policy in real turtlebot in the mapped environment:
- [Turtlebot] Launch the mobile base (see Step 2a)
- [Turtlebot] Launch the LiDAR (see Step 2b)
- [Host computer] Launch localization and navigation
```
source ~/tb2.bash
source ~/catkin_ws/devel/setup.bash
roslaunch turtlebot_navigation laser_amcl_demo.launch map_file:=$HOME/map.yaml
```
  This step is ready if the terminal shows "odom received".
- [Host computer] Launch rviz (see Step 2d)
  To calibrate localization, use "2D pose estimate" to correct the initial pose of robot, and then use "2D navigation" to navigate the robot around until the localization particles converge.
- [Host computer] To filter out the static obstacles on the map and improve the people detection,
```
source ~/tb2.bash
source ~/catkin_ws/devel/setup.bash
cd ~/catkin_ws/src/2D_lidar_person_detection 
python findloc_bgrm.py path_to_the_map_created_in_step2
```
- [Host computer] Run the DR-SPAAM people detector:
```
source ~/tb2.bash
source ~/catkin_ws/devel/setup.bash
source ~/virtual_envs/tb2/bin/activate # activate the virtual environment created in Setup -> Host computer -> Step 6
roslaunch dr_spaam_ros dr_spaam_ros.launch
```
- [Turtlebot] Launch the Realsense T265 camera using t265.launch from this repo
```
roslaunch t265.launch
```
  - Skip this step if you're planning to use LiDAR for robot localization.
- [Host computer] cd into the crowd navigation repo,
  - in trained_models/your_output_dir/arguments.py, change env-name to 'rosTurtlebot2iEnv-v0'
  - in trained_models/your_output_dir/configs/config.py, change configurations under sim2real if needed
  - then run
```
python test.py 
```
  Type in the goal position following the terminal output, and the robot will execute the policy if everything works.

To-Do list

The robot localization from LiDAR and T265 are redundant. Remove the dependency on T265 later.

Disclaimer

We only tested our code in the above listed hardware and software settings. It may work with other robots/versions of software, but we do not have any guarantee.
If the RL training does not converge, we recommend starting with an easier setting (fewer humans, larger circle radius, larger robot speed, etc) and then gradually increase the task difficulty during training.
The performance of our code can vary depending on the choice of hyperparameters and random seeds (see this reddit post). Unfortunately, we do not have time or resources for a thorough hyperparameter search. Thus, if your results are slightly worse than what is claimed in the paper, it is normal. To achieve the best performance, we recommend some manual hyperparameter tuning.

Citation

If you find the code or the paper useful for your research, please cite the following papers:

@inproceedings{liu2022intention,
  title={Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph},
  author={Liu, Shuijing and Chang, Peixin and Huang, Zhe and Chakraborty, Neeloy and Hong, Kaiwen and Liang, Weihang and Livingston McPherson, D. and Geng, Junyi and Driggs-Campbell, Katherine},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2023}
}

@inproceedings{liu2020decentralized,
  title={Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning},
  author={Liu, Shuijing and Chang, Peixin and Liang, Weihang and Chakraborty, Neeloy and Driggs-Campbell, Katherine},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={3517-3524}
}

Credits

Other contributors:
Peixin Chang
Kaiwen Hong
Jerry Wang Eric Liang

Contact

If you have any questions or find any bugs, please feel free to open an issue or pull request.

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