Home Service Robot

The goal of this project is to bring together a couple of ROS/RViz/Gazebo concepts:

• World and odometry simulation in Gazebo,
• Visualization of the odometry, sensor and localization data, as well as virtual objects in RViz,
• SLAM via gmapping, and
• Adaptive Monte-Carle Localization via the AMCL package.

The robot in this scenario is tasked with picking up a virtual item from a start position, visualized by a red sphere in RViz, in order to bring to to a goal position.

Navigation to the start and end positions is implemented in pick_objects_node.cpp and visualization of the item is implemented in add_markers_node.cpp.

Note that in both cases, the start and goal positions are provided statically, and the whole implementation can be (drastically) improved by publishing pick-up and drop-off locations via a dedicated topic.

Previous projects have shown that attempting to run SLAM on a synthetic world like this are bound to fail. Here's the intermediate result after leaving from the start position and taking a left turn around the center triangular room, back to the start position:

Because this clearly fails, the map was instead created using the pgm_map_creator node directly from Gazebo and then retouched to add in doors. Adding more (natural) textures and items to this world would have improved SLAM, but also deviated a bit from the main idea of this repo. Again, investing more time here would make this quite an interesting project overall.

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To run the code, execute either one of these scripts; each of them will spin up all nodes required for the individual test task at hand:

• src/scripts/test_slam.sh for testing SLAM,
• src/scripts/test_navigation.sh for testing AMCL navigation,
• src/scripts/pick_objects.sh for testing navigation automation,
• src/scripts/add_markers.sh for testing markers,
• src/scripts/home_service.sh for running the actual project.

Make sure to catkin_make and source devel/setup.bash the workspace before.

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To build the project, you can try executing ./run-nvidia.sh to drop into an X11 aware Docker container with NVIDIA GPU support.

Docker environment

An issue exists with ROS Kinetic and TurtleBot in Docker, since the TurtleBot packages rely on librealsense, which in turn depends on a Kernel module that cannot be installed. This is pointed out by IntelRealSense/librealsense#4781, which (thankfully) also provides a workaround. That workaround is implemented in docker/build-librealsense.sh and is executed as part of the Docker image build in build-docker.sh.

Building with CLion IDE

Note: This does not really work, as CLion will be unable to find generated headers. It's still a bit better than doing everything the hard way.

The full requirements for setting up CLion are given in the sunsided/robond-ros-docker repository. In short, run SSHD in Docker, configure a Remote Host build to connect to it, then configure the your build settings for ROS. For this repo and the included Dockerfile, this configuration will do:

CMake options:

-DCATKIN_DEVEL_PREFIX:PATH=/workspace/devel -DCMAKE_PREFIX_PATH=/workspace/devel;/opt/ros/kinetic;/opt/ros/kinetic/share

Environment:

ROS_ROOT=/opt/ros/kinetic/share/ros;ROS_PACKAGE_PATH=/workspace/src:/opt/ros/kinetic/share;ROS_MASTER_URI=http://localhost:11311;ROS_PYTHON_VERSION=2;ROS_VERSION=1;ROSLISP_PACKAGE_DIRECTORIES=/workspace/devel/share/common-lisp;ROS_DISTRO=kinetic;ROS_ETC_DIR=/opt/ros/kinetic/etc/ros;PYTHONPATH=/opt/ros/kinetic/lib/python2.7/dist-packages;PKG_CONFIG_PATH=/workspace/devel/lib/pkgconfig:/opt/ros/kinetic/lib/pkgconfig:/opt/ros/kinetic/lib/x86_64-linux-gnu/pkgconfig;LD_LIBRARY_PATH=/workspace/devel/lib:/opt/ros/kinetic/lib:/opt/ros/kinetic/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH;PATH=/opt/ros/kinetic/bin:$PATH