Introduction - SaraPettinari/fame GitHub Wiki


The FaMe framework development phases are defined for designing and executing a BPMN-driven MRS.

The modeling phase corresponds to the first step in developing an MRS using the FaMe framework. It consists of a disciplined use of the BPMN notation allowing the definition of a collaboration diagram that can represent at a high level of abstraction the behaviors of the robots involved in an MRS.

The configuration phase enriches the modeled collaboration with all the information needed for execution. It produces as output an executable collaboration diagram used in the following phase to command each robotic device, thus guaranteeing a decentralized execution of the MRS.

The last phase is the execution of the BPMN collaboration directly on each involved robot, without the need for any direct translation into code. This is made possible through BPMN engines, one for each robot, that enact only the process associated with each considered device. As a result of this phase, the framework enables the MRS execution both in simulated and real environments.

Reference papers:

  • Flavio Corradini, Sara Pettinari, Barbara Re, Lorenzo Rossi, Francesco Tiezzi. A BPMN-driven framework for Multi-Robot System development. In: Robotics and Autonomous Systems, vol. 160, pp. 104322, 2022, ISSN: 0921-8890.
  • F. Corradini, S. Pettinari, B. Re, L. Ruschioni, F. Tiezzi. Enhancing Compatibility in QoS Communication for the Internet of Robotic Things. ER Forum/Posters/Demos, inpress,, 2023.
  • Khalid Bourr, Flavio Corradini, Sara Pettinari, Barbara Re, Lorenzo Rossi, Francesco Tiezzi. Disciplined use of BPMN for mission modeling of Multi-Robot Systems. In: Proceedings of the Forum at Practice of Enterprise Modeling (PoEM 2021), pp. 1–10,, 2021.

Find the BibTeX here.



The FaMe framework is supported by a toolchain composed of software and artifacts, developed for experimenting with the model-driven development approach for ROS-based MRSs. The modeling, configuration, and execution phases can be fully automated through the use of the provided toolchain. Specifically, two packages support the FaMe framework. The FaMe-modeler is a web app facilitating the modeling and configuration phases, whereas FaMe-ROS is a ROS package containing the splitter and engine nodes. This design enables the reuse and integration of these packages with different robots. Communication between packages is enabled by the ros2-web-bridge, which provides a JSON interface to ROS, allowing any client to send JSON to publish or subscribe to ROS topics.