ROVI System

Robotics and computer vision system for pick-and-place tasks with ROS/Gazebo/MoveIt.

• Overview
• Installation
• Usage
  • Running the project
  • Experiments
  • Configuration (VS Code)
  • Guidelines
• License
• Acknowledgments

Overview

The vision-based pick-and-place pipeline is realized within a simulated environment using the ROS/Gazebo/MoveIt framework. A workcell consists of: a UR5 manipulator mounted onto a table with designated pick and place areas, objects, obstacles, and various perception sensors.

The project is composed of several project-specific and external ROS packages, as well as other dependencies.

Packages

Package	Description
rovi_system	Main project package with examples and tests
rovi_models	Gazebo models and worlds for the the ROVI workcell
rovi_planner	Interpolation-based trajectory generation
rovi_vision	Computer vision-based pose estimation methods for objects in the ROVI workcell
ur5_ros	Integration of UR5 robot into ROS/Gazebo/MoveIt environment
ros_utils	Collection of modern utilities for the ROS/Gazebo/MoveIt workflow
qp_oases	Port of qpOASES library to ROS

For more information, please refer to the README.md of a specific package.

Dependencies

• ROS (noetic) - framework for robot operation
• Gazebo - robot simulation environment
• rosdep - management of ROS system dependecies
• vcstool - automated workspace configuration
• catkin_tools - command line tools for working with catkin workspaces
• export_fig + Ghostscript - exporting figures in MATLAB (optional)

Installation

The project is tested on Ubuntu 20.04.3 LTS. Built using catkin_tools with CMake 3.4 and gcc 9.3.0-17.

Installing ROS and dependencies

1. Install ROS (Desktop-Full Install) and rosdep (guide)
2. Install vcstool (guide)

sudo apt install python3-vcstool

3. Install catkin_tools (guide)

sudo apt install python3-catkin-tools

Installing project (workspace)

Make sure you have Git SSH configured properly as per this guide.

Download setup.bash (← right click and save as) to where the workspace should be created.

Open a terminal, navigate to the file, make it executable with chmod +x setup.bash, and execute the file in the current shell as source setup.bash. This will create a catkin workspace and download all necessary packages.

Usage

Running the project

Navigate to the rovi_ws workspace (or run rovi_ws in terminal). The rovi_ws command will automatically source the workspace and navigate to its directory.

Build the workspace using:

catkin build

From the root of the workspace, source the environment variables by running source devel/setup.bash.

Launch the workcell by running:

roslaunch rovi_system workcell.launch

The workcell launch file can be configured using several arguments, for example:

roslaunch rovi_system workcell.launch ee:=wsg50 controller:=ur5_joint_position_controller

An overview of the arguments is located in the workcell.launch file.

Experiments

Experiments in rovi_system are contained in the rovi_system/tests/ directory. An experiment of <name> can be implemented in either C++ or Python, and is structured as:

Structure of an experiment in rovi_system

rovi_system/tests/                 # directory for all experiments in rovi_system
|
└── <name>/                        # experiment directory
    |
    ├── img/                       # exported plots
    ├── data/                      # directory with time-stamped trials
    |   ├── 20210105_000322/      
    |   └── ...
    |
    ├── test_<name>.cpp            # source code for experiment (ROS node named test_<name>)
    ├── test_<name>.py             # python code for experiment
    ├── test_<name>.launch         # launch file for experiment
    ├── test_<name>.m              # MATLAB code for data manipulation/plotting using export_fig
    └── README.md                  # documentation of experiment

A C++ experiment is automatically added as a ROS node named test_<name> (by rovi_system/CMakeLists.txt) and can be launched using rosrun or roslaunch (if provided). Use rovi_system.h and scripts/rovi_system.m for helper functions (get experiment/data/img directory, plotting etc.) - see the template experiment for example code.

Configuration (VS Code)

Since IntelliSense is utter trash for larger projects, it is recommended to use the clangd extension as the language server, together with catkin-tools-clangd python package to generate the compile_commands.json for clangd.

The ROS extension is also a nice addition when working in VS Code. However, the cpp_properties.json file it generates for IntellSense is bugged; change the line /usr/ros/noetic/** to /usr/ros/noetic/ to fix include problems.

Guidelines

From the root of the workspace (i.e. after running rovi_ws), run catkin build (or catkin build_compile_cmd if using clagngd extension) to build the workspace. The coding conventions are defined by the .clangformat (TODO), summarized as:

Coding conventions

• Indent with tabs, align with spaces
• Comments in lower-case, add URLs to external resources
• Consistent interfaces accross the project (e.g. args and return values)
• Always review the code and examples of a package before adding new code
• Examples of methods/classes etc. is a must (in /examples)
• Commit in blocks of relevant code with short and descriptive messages (typically all lower-case)
• Proper includes, cmake and package manifest
• Segregate code properly in packages; generic utilities go in ros_utils pkg
• ROS Best Practices
• Look at rovi_system/examples/code_conventions.cpp (TODO) for inspiration
• Add any bugs/issues/todos to GitHub Issues

License

No license has been decided yet.

Acknowledgments

Thanks to SDU for moral support.