Automatic Configuration Recognition Methods in Modular Robots

Authors:
Michael Park;Sachin Chitta;Alex Teichman;Mark Yim
Affiliations:
GRASP Laboratory, University of Pennsylvania, USA;GRASP Laboratory, University of Pennsylvania, USA;GRASP Laboratory, University of Pennsylvania, USA;GRASP Laboratory, University of Pennsylvania, USA
Venue:
International Journal of Robotics Research
Year:
2008

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Abstract

Recognizing useful modular robot configurations composed of hundreds of modules is a significant challenge. Matching a new modular robot configuration to a library of known configurations is essential in identifying and applying control schemes. We present three different algorithms to address the problem of (a) matching and (b) mapping new robot configurations onto a library of known configurations. The first method solves the problem using graph isomorphisms and can identify configurations that share the same underlying graph structure, but have different port connections amongst the modules. The second approach compares graph spectra of configuration matrices to find a permutation matrix that maps a given configuration to a known one. The third algorithm exploits the unique structure of the problem for the particular robots used in our research to achieve impressive gains in performance and speed over existing techniques, especially for larger configurations. With these three algorithms, this paper presents novel solutions to the problem of configuration recognition and sheds light on theoretical and practical issues for long-term advances in this important area of modular robotics. Results and examples are provided to compare the performance of the three algorithms and discuss their relative advantages.