
Departmental Papers (ESE)
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Hierarchical Navigation of Disks
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 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 which 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.
For more information: Kod*lab
Sponsor Acknowledgements
This work was supported in part by AFOSR under the CHASE MURI FA9550–10–1−0567 and in part by ONR under the HUNT MURI N00014070829.
Document Type
Journal Article
Subject Area
GRASP, Kodlab
Date of this Version
3-21-2016
Publication Source
IEEE Transactions on Robotics
Volume
32
Issue
2
Start Page
352
Last Page
371
DOI
10.1109/TRO.2016.2524018
Keywords
Multirobot systems, navigation functions, formation control, swarm robots, configuration space, coordinated motion planning, hierarchical clustering, cohesion, segregation
Bib Tex
@Article{arslan_guralnik_kod_TRO2016, Title = {Coordinated Robot Navigation via Hierarchical Clustering}, Author = {Omur Arslan and Dan P. Guralnik and Daniel E. Koditschek}, Journal = {IEEE Transactions of Robotics}, Year = {2016},
Month = {April},
Volume = {32},
Number = {2},
Pages = {352 - 371},
Doi = {10.1109/TRO.2016.2524018}
}
Date Posted: 26 October 2016
This document has been peer reviewed.