chushuiniba / docking-sim

MRSD project of Team Align

Autonomous Docking for an Ackermann Vehicle

Team Align

Please refer to our website for more detailed system implementation.

Setup and Usage

This repo has been tested on Ubuntu 18.04 (ROS Melodic)

Requirements:

• Ubuntu 18.04+
• ROS-Melodic
• python 2.7
• move_base
• GTSAM
• Autoware
• apriltag_ros
• teb-local-planner

Docking Simulation

Clone the repo in catkin workspace and run

catkin_make
source devel/setup.bash
roslaunch align_gazebo align.launch

In other terminals (Do not forget to run the env with Python 2.7 and to source ROS)

sh run_keyop.sh #To manually drive the vehicle
roslaunch pod_localizer goal_pub.launch #For Localizing Pod
roslaunch align_navigation mapless_move_base.launch #For Running Planner
rosrun align_gazebo pure_pursuit.py #To plan a path till goal
rosrun align_navigation goal_publisher.py #To use other planners

Autoware Simulation

First, for enabling our pod and chassis configuration

• Replace the ..path-to-autoware/autoware/install/vehicle_model with docking-sim/vehicle_model
• Replace the ..path-to-autoware/autoware/install/vehicle_gazebo_simulation_launcher with docking-sim/vehicle_gazebo_simulation_launcher

Run (from Autoware installed folder) to setup simple world

source install/setup.bash 
#Make sure autoware environment is activated
roslaunch vehicle_gazebo_simulation_launcher gazebo_launcher.launch world_name:=simple gpu:=true

Run Autoware runtime manager

#Run Autoware and Configure for use - Required is path planner, NDT localizer and Rviz
roslaunch runtime_manager runtime_manager.launch

Move to docking-sim folder, source and run the following

roslaunch align_gazebo autoware.launch #HMS, Obstacle Detection and PHZ Identification
#HMS TESTS
rosrun hms_client scan_dummy.py
rosnode kill /obstacle_2d
rosrun obstacle_2d obstacle_2d

#Docking
roslaunch align_navigation mapless_move_base.launch
roslaunch pod_localizer goal_pub_autoware.launch #For Localizing Pod
rosrun align_navigation goal_publisher.py #For Approach Navigation

For running the chassis using keyboard

rosrun align_gazebo ackermann_drive_to_cmd_vel.py
sh run_keyop.sh #To manually drive the vehicle

To use Fake Localization instead of Autoware

rosrun align_gazebo tf_broadcaster_autoware.py

Future Updates

To install GTSAM (Latest Version)

sudo apt-add-repository ppa:bernd-pfrommer/libgtsam
sudo apt update
sudo apt install libgtsam-unstable4 libgtsam4 libgtsam-dev libgtsam-unstable-dev

If Previously installed

sudo apt remove gtsam
sudo add-apt-repository --remove ppa:bernd-pfrommer/gtsam

Citation

Please cite outwork if you use or extend this

Support

Send a mail to Sachit Mahajan

References

• Dubins, L.E. (July 1957). "On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents". American Journal of Mathematics 79 (3): 497–516
• LaValle, S. M. (2006). "Planning Algorithms". Cambridge University Press
• Shkel, A. M. and Lumelsky, V. (2001). "Classification of the Dubins set". Robotics and Autonomous Systems 34 (2001) 179–202
• Walker, A. (2011). "Hard Real-Time Motion Planning for Autonomous Vehicles", PhD thesis, Swinburne University.
• Royce, S. (2008). "Evolutionary Control of Autonomous Underwater Vehicles". PhD thesis, RMIT
• Reeds, J., & Shepp, L. (1990). Optimal paths for a car that goes both forwards and backwards. Pacific journal of mathematics, 145(2), 367-393.

For 'april_tag_ros' If you use this code, please kindly inform Danylo Malyuta (to maintain a list here of research works that have benefited from the code) and cite:

• D. Malyuta, C. Brommer, D. Hentzen, T. Stastny, R. Siegwart, and R. Brockers, “Long-duration fully autonomous operation of rotorcraft unmanned aerial systems for remote-sensing data acquisition,” Journal of Field Robotics, p. arXiv:1908.06381, Aug. 2019.
• C. Brommer, D. Malyuta, D. Hentzen, and R. Brockers, “Long-duration autonomy for small rotorcraft UAS including recharging,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, p. arXiv:1810.05683, oct 2018.
• J. Wang and E. Olson, "AprilTag 2: Efficient and robust fiducial detection," in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)'', October 2016.