High-level Perception and Control for Autonomous Reconfiguration of Modular Robots (NSF CPS)
Mark Campbell, Mark Yim (UPenn)
The goal of this research is to develop theory, hardware and computational infrastructure to enable automatically transforming user-defined, high-level tasks into correct, low-level perception informed control and configurations for modular robots.
1. Hardware: designing and building a robust reconfigurable hardware platform based on UPenn's SMORES work.
2. The Library: develop (manually and automated tools) perception, locomotion, manipulation and reconfiguration components as well as a mapping between the low-level control of a modular robot and basic properties (semantics and continuous metrics).
3. High-level control synthesis: Develop methods for synthesizing high level, information informed, controllers for modular robots based on the library entries, their correct sensor based sequencing, and a grammar and parser designed to address the unique characteristics of modular self-reconfigurable robots.