The main goal of this project is to develop a generator of random shapes for modular robots and integrate it into an existing modelling framework.
Modular robots are composed of entities that are able to compute and communicate with their neighbors by using sensors and actuators. We consider modular robots, which are either shaped and handled by the user (such as Blinky Blocks), or capable of sliding along the faces of their neighbours (as simulated in VisibleSim). These systems perform dynamic changes depending on their execution environment (activated or desactivated entities), and the individual goals of the entities may change as well.
As part of a previous Master project we have developed a model of robots composed of Blinky Blocks using DR-BIP  — an extension of the BIP  component-based design framework for dynamically reconfigurable systems. However, the initial shape of the robot must still be defined statically by the user.
The first goal of this project is to design a generator of random shapes for modular robots, satisfying a set of user-defined constraints. The second goal is to integrate this random generator into the DR-BIP model and validate it through the simulation of power distribution through robots of different shapes.
You will learn the principles of rigorous system design based on formal operational semantics and get an in-depth understanding of BIP, a state-of-the-art component-based framework. Successful internship can lead to a research publication.
Good analytical skills will definitely be required. The candidate must have good understanding of Finite State Machines and some experience in C++.
Contact and application
Ananda Basu, Saddek Bensalem, Marius Bozga, Jacques Combaz, Mohamad Jaber, Thanh-Hung Nguyen, and Joseph Sifakis: Rigorous component-based system design using the BIP framework. IEEE Software 28(3):41–48 (2011)
[Website | PDF]
Rim El Ballouli, Saddek Bensalem, Marius Bozga, and Joseph Sifakis: Programming dynamic reconfigurable systems. International Journal on Software Tools for Technology Transfer (2021)