Theoretical methods are commonly used in swarm robotics research to abstractly explain robotic systems. The OBLOT method, which describes robots as simple systems, all identical, without memory, and unable to interact with one another, is a common theoretical model in robotics research.

MOBLOT, an extension of the OBLOT model that can be implemented to a broader variety of swarm robotics systems, was recently developed by researchers at the University of L'Aquila and the University of Perugia in Italy. The ways in which atoms naturally organize themselves to shape matter inspired this new model, which was presented at the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021).

According to the report, "MOBLOT is a new model in the context of theoretical swarm robotics," Alfredo Navarra, one of the researchers who carried out the study, told TechXplore. "The acronym stands for Molecular OBLivious robOTs since the inspiration comes from nature: Like atoms combining themselves to form molecules, in MOBLOT, simple (and very weak) robots can move to form more complex computational units (which are also called molecules in the model), having extended and different capabilities with respect to robots."

Molecular robots, once shaped, can assemble themselves into any form based on specific compositional properties, producing a robotic "matter." If other inputs or stimuli are introduced after a given shape has been formed, the molecules may automatically rearrange (i.e., self-reconfigure) their positions to alter the shape.

The MOBLOT model, in comparison to the OBLOT model and other swarm robotics methods, can be used in a wider variety of situations where the symmetry of a robot swarm formation is broken. The model essentially formalizes the actions of very simple robots that can accumulate to form more complicated robotic structures (the "molecules"), which can then aggregate to form different kinds of final compounds (the "matter").