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Coordinated Robot Navigation via Hierarchical Clustering.

Coordinated Robot Navigation via Hierarchical Clustering.

Authors :
Arslan, Omur
Guralnik, Dan P.
Koditschek, Daniel E.
Source :
IEEE Transactions on Robotics. Apr2016, Vol. 32 Issue 2, p352-371. 20p.
Publication Year :
2016

Abstract

We introduce the use of hierarchical clustering for relaxed deterministic coordination and control of multiple robots. Traditionally, an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of the hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of $n$ perfectly sensed and actuated Euclidean spheres in a $d$-dimensional ambient space (for arbitrary $n$ and $d$). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller that is quadratic in $n$ and algebraic in $d$ and prove that its execution brings all but a measure zero set of initial configurations to the desired goal, with the guarantee of no collisions along the way. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
15523098
Volume :
32
Issue :
2
Database :
Academic Search Index
Journal :
IEEE Transactions on Robotics
Publication Type :
Academic Journal
Accession number :
114283798
Full Text :
https://doi.org/10.1109/TRO.2016.2524018