High-level perception and control for autonomous reconfigurable modular robots

The objective of this research is to develop the theory, hardware and computational infrastructure that will
enable automatically transforming user-defined, high-level tasks into correct, low-level perception
informed control and configurations for modular robots. Modular robots are composed of simple
individual modules with limited sensing and actuation; while each module can locomote in the
environment, connecting multiple modules in different configurations allows modular robots to perform
complex actions such as climbing, manipulating objects and traveling in unstructured environments.
Furthermore, modular robots can divide into multiple robots and then reconfigure as one.
The posters describe the progress in the first three years of the project; the hardware development of the
SMORES modules, incorporating sensors as specialty modules, creating hardware-constrained perception
algorithms, intelligent planning of motion/reconfiguration for shape estimation, creating a framework for
composing configurations and gaits from basic components using the notion of series-parallel graphs,
creating a game-based environment for crowdsourcing controller and configuration design (can be found
at http://vsparc.org) and developing a library of controllers to create high-level behaviors for modular
robots.