PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A characterization of the reconfiguration space of self-reconfiguring robotic systems.
Subramanian Ramamoorthy and Thomas Larkworthy
Robotica Volume 29, Number 1, pp. 73-85, 2011.

Abstract

Motion planning for self-reconfiguring robots can be made efficient by exploiting potential reductions to suitably large subspaces. However, there are no general techniques for identifying suitable restrictions that have a positive effect on planning efficiency. We present two approaches to understanding the structure that is required of the subspaces, which leads to improvement in efficiency of motion planning. This work is presented in the context of a specific motion planning procedure for a hexagonal metamorphic robot. First, we use ideas from spectral graph theory – empirically estimating the algebraic connectivity of the state space – to show that the HMR model is better structured than many alternative motion catalogs. Secondly, using ideas from graph minor theory, we show that the infinite sequence of subspaces generated by configurations containing increasing numbers of subunits is well ordered, indicative of regularity of the space as complexity increases. We hope that these principles could inform future algorithm design for many different types of self-reconfiguring robotics problems.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7612
Deposited By:Subramanian Ramamoorthy
Deposited On:17 March 2011