This is a branch of Victoria Tuck's Multi Robot Task Allocation Stack! made specifically for WSL2 on windows.
It takes as a given that the WSL2 environment is set up and that nvidia-smi is posting on the command line.
In case you are using native linux I strongly suggest to start from the original project as I have done some changes on container files which might break some functions.
- NVIDIA discrete GPU (for gazbeo)
- Diplay should use NVIDA and not integrated graphics
- Docker installed (docker and nvidia-docker)
- https://docs.docker.com/engine/install/ubuntu/
- https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
This documentation is for main branch of multi-robot-task-allocation-stack repository and of this repository.
To setup the SMrTa task allocator, first add the submodules running
git submodule init
git submodule update
cd colcon_ws/src/social_navigation/social_navigation_py/social_navigation_py/SMrTa
git submodule init
git submodule update
Then install the bitwuzla Python bindings dependencies. Bitwuzla can be installed by running
sh setup_bitwuzla.sh
from the SMrTa folder.
Then to build the environment, run
# For WSL2
COMPOSE_PROFILES=wsl docker-compose up
# For Linux
COMPOSE_PROFILES=linux docker-compose up
docker compose up -d
docker exec -it multi-robot-task-allocation-stack-ros-1 bash
from the highest level of the repository where <service> is ubuntu or wsl depending on your operating system.
Now, you have to first build the colcon (ROS2) workspace. Navigate to
cd /home/colcon_ws
colcon build --symlink-install
Note sometimes ROS does not figure out package dependency order properly when multiple ROS packages are present. In this case, it may take multiple runs of colcon build to be successful. If one error is shown, after this step, you can still proceed successfully.
Now source the installed packages with following command
source install/local_setup.bash
source /usr/share/gazebo/setup.sh
Finally, to give docker environment permission to use graphics of hist machine, run the following command from host machine
xhost +
Choose nominal controller gains and CBF parameters in cbf_obstacle_controller.py.
To change the number of controllers that are started, change the case config file in multi_cbf.launch.py to your config file or change the name of the file to case_config.yaml
Then run the code in the following sequence. To aid in implementation, several aliases are defined in the ~/.bashrc file upon docker build. Six terminals will be needed; run the docker exec command in each terminal. Wait for each of the below commands to complete before running the next.
- To launch the gazebo environment with the robot inside it
rgazebo input_file:=<path_to_setup_file>
Example:
rgazebo input_file:=/home/colcon_ws/src/social_navigation/social_navigation_py/social_navigation_py/robot_setup_6.json
Note: After this step, the Gazebo environment should the robots (which are in the middle of blue circles). Upon start-up, the robot installation will occasionally fail. If this occurs, exit and rerun the above command.
- To launch the ROS2 navigation stack (to use its planners)
rnav2
- To start humans moving
rsfm
Further humans can be added by adding in or uncommenting "actors" in the world file under colcon_ws/src/social_navigation/social_navigation/worlds.
- To launch multiple robotic agent navigation stacks
ros2 launch aws_robomaker_hospital_world main.launch.py input_file:=<path_to_setup_file>
Example:
ros2 launch aws_robomaker_hospital_world main.launch.py input_file:=/home/colcon_ws/src/social_navigation/social_navigation_py/social_navigation_py/robot_setup_6.json
Note: After this step, the rviz environment should include an arrow for each robot and colored buffers around the walls. If it does not, rerun this command.
- To launch the navigation stack wrapper
rcplan --ros-args -p "robots:=<list_of_robot_names>"
Example:
rcplan --ros-args -p "robots:=["robot1", "robot2", "robot3", "robot4", "robot5", "robot6"]"
- To launch the room queues
rqueues
- This will start moving the robots. For task allocation and high-level path planning for a set of agents:
rcdis -p input_file:=<setup_file>
Example:
rcdis -p input_file:=/home/colcon_ws/src/social_navigation/social_navigation_py/social_navigation_py/robot_setup_6.json
The default implemented task allocator is the SMrTa approach which can be found here.
To run the experiment included in our paper, run the examples from the 6 steps above. Calculation time data can be processed afterwards with
python3 process_data.py
which will print out the results of Table 1.
Many of the nodes have been combined into single launch files for ease of use. However, additional aliases have been included to run these nodes separately when debugging.
- To get human states from gazebo and to find the closest obstacle points to each robot
rcsetup input_file:=<setup_file>
- To start the user controller for multiple robots:
multi_rcbf input_file:=<setup_file>
Example:
multi_rcbf input_file:=case_config_3.yaml
- When the controller status is ONLINE, run the following command to set goal and run controller simulation
rcpub
- To manage the task assignment for each agent:
rcset input_file:=<setup_file>
The TravelTimeCollector node in travel_time_collector.py has been provided in order to collect travel time data. This can be run with:
rctimecollect -p time_collection_params:=<params_file> -p save_file:=<save_file>
where <params_file> is the name of a json file containing the number of iterations, save mode, desired format, map locations, and location ids and <save_file> is where the travel time information should be saved.
To integrate with Scenic, navigate to the src folder then install with the following commands:
git clone git:github.com:Kai-X-Org/ScenicROS2.git
cd ScenicROS2
python3 -m pip install -e .
The bookshelf example can be run with
cd src/scenic/simulators/Gazebo
scenic test.scenic --simulate
Further documentation can be found on the documentation page.