ARCOR (formerly known as ARTable) - the main repository

ARCOR - vision of a near future workspace, where human and robot may safely and effectively collaborate. Our main focus is on human-robot interaction and especially on robot programming - to make it feasible for any ordinary skilled worker. The interaction is based mainly on interactive spatial augmented reality - combination of projection and touch sensitive surface. However, more modalities are integrated or currently under development.

Repositories / packages

This repository holds the main components of the system, which are not specific to any particular setup (combination and type of components) or robot:

• art_brain - central node which communicates with robot, manages program execution, holds current system state etc.
• art_bringup - to launch the system.
• art_calibration - AR marker-based mutual calibration of cameras.
• art_collision_env - manages detected as well as artificial objects within the workspace.
• art_db - permanent storage for object types, programs, etc.
• art_instructions - for each supported instruction there is its definition in yaml and respective classes for art_brain and art_projected_gui. Those classes are loaded on startup based on /art/instructions parameter.
• art_led - RGB LED strip interface.
• art_projected_gui - shows system state, allows to set program parameters, etc.
• art_projector - calibrates projector wrt. Kinect and displays scene generated by art_projected_gui.
• art_simple_tracker - not a real tracker, it "tracks" objects based on already assigned IDs and performs position/orientation filtering from multiple detectors.
• art_sound - a sound interface: plays sound for selected system events (error).
• art_table_pointing - uses Kinect skeleton tracking to compute where user points on the table.
• art_touch_driver - reads data from touch foil (which is HID device) a publishes it as ROS messages.

Additional repositories:

• arcor-msgs - ROS messages, services, actions.
• arcor-utils - Python helper classes.
• arcor-detectors - detectors (currently, only AR code detector wrapper).

For each integrated robot, there are two repositories: one with custom packages providing high-level functions compatible with arcor ROS API and one with implementation of art_brain plugin (-interface one):

• PR2
  • https://github.com/robofit/arcor-pr2
  • https://github.com/robofit/arcor-pr2-interface
• DOBOT Magician
  • https://github.com/robofit/arcor-dobot
  • https://github.com/robofit/arcor-dobot-interface
• Empty (dummy) arm
  • https://github.com/robofit/arcor-empty-arm

Currently supported setups (see links for further information):

• arcor setup 1
• arcor setup 2
• arcor setup 3

Any supported setup may be used with any supported robot (or even without one).

Functionality

The system has two main modes: setting program parameters and program execution.

The video below briefly introduces the system and shows how we did its first UX testing:

Currently, the robot program has to be created beforehand (e.g. using script like this. Then, program parameters could be easily set using the projected interface. In order to make setting parameters as simple as possible, the system is based on complex instructions, with high-level of abstraction (for supported instructions see instructions.yaml).

API

All topics, parameters and services can be found in /art namespace.

TBD

Installation

TBD

Contributing

• Follow PyStyleGuide or CppStyleGuide
  • for Python, you may use pre-commit hook to automatically format your code according to PEP8 (just copy the file into .git/hooks).
• Use catkin_lint to check for common problems (catkin_lint -W2 your_package_name)
• Use roslint to run static analysis of your code.
• Ideally, create and use unit tests.
• Feel free to open pull requests!

Publications

• MATERNA Zdeněk, KAPINUS Michal, BERAN Vítězslav, SMRŽ Pavel a ZEMČÍK Pavel. Interactive Spatial Augmented Reality in Collaborative Robot Programming: User Experience Evaluation. In: Robot and Human Interactive Communication (RO-MAN). NanJing: Institute of Electrical and Electronics Engineers, 2018 (to be published).
• MATERNA Zdeněk, KAPINUS Michal, BERAN Vítězslav a SMRŽ Pavel. Using Persona, Scenario, and Use Case to Develop a Human-Robot Augmented Reality Collaborative Workspace. In: HRI 2017. Vídeň: Association for Computing Machinery, 2017, s. 1-2. ISBN 978-1-4503-4885-0.
• MATERNA Zdeněk, KAPINUS Michal, ŠPANĚL Michal, BERAN Vítězslav a SMRŽ Pavel. Simplified Industrial Robot Programming: Effects of Errors on Multimodal Interaction in WoZ experiment. In: Robot and Human Interactive Communication (RO-MAN). New York City: Institute of Electrical and Electronics Engineers, 2016, s. 1-6. ISBN 978-1-5090-3929-6.