Optimal automated path planning for infinitesimal and real-sized particle assemblies

The present article introduces an algorithm for path planning and assembly of infinitesimal and real-sized particles by using a distance and path based permutation algorithm. The main objective is to define non-overlapping particle paths subject to minimal total path length during particles position...

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Main Authors: Alp Karakoc, Ertugrul Taciroglu
Format: Article
Language:English
Published: AIMS Press 2017-07-01
Series:AIMS Materials Science
Subjects:
Online Access:http://www.aimspress.com/Materials/article/1546/fulltext.html
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spelling doaj-726bfcd1a1814d31bd87bac133061ff52020-11-25T01:58:18ZengAIMS PressAIMS Materials Science2372-04842017-07-014484785510.3934/matersci.2017.4.847matersci-04-00847Optimal automated path planning for infinitesimal and real-sized particle assembliesAlp Karakoc0Ertugrul Taciroglu1Civil and Environmental Engineering Department, University of California Los Angeles, 90095, Los Angeles, USACivil and Environmental Engineering Department, University of California Los Angeles, 90095, Los Angeles, USAThe present article introduces an algorithm for path planning and assembly of infinitesimal and real-sized particles by using a distance and path based permutation algorithm. The main objective is to define non-overlapping particle paths subject to minimal total path length during particles positioning and assembly. Thus, a local minimum is sought with a low computational cost. For this reason, an assignment problem, to be specific Euclidean bipartite matching problem, is presented, where the particles in the initial (random selection) and final (particle assembly) configurations are in one-to-one correspondence. The cost function for particle paths is defined through Euclidean distance of each particle between the initial and final configurations. Principally, a cost flow problem is formed and solved by determining an optimal permutation subject to the total Euclidean distance of the particles and their non-overlapping paths. Monte Carlo simulations are carried out for non-overlapping paths; thus, non-colliding particles, and then total path distances of the obtained sets are minimized, resulting in an optimal solution which may not be necessarily the global optimum. Case studies on basic and complex shaped infinitesimal and real-sized particle assemblies are shown with their total costs, i.e., path lengths. It is believed that the present study contributes to the current efforts in optical trapping automation for particle assemblies with possible applications, e.g., in the areas of micro-manufacturing, microfluidics, regenerative medicine and biotechnology.http://www.aimspress.com/Materials/article/1546/fulltext.htmlEuclidean bipartite matchingcost flowinfinitesimal particle assemblyreal-sized particle assemblyoptical trappingmicro-manufacturingmicrofluidicsregenerative medicine
collection DOAJ
language English
format Article
sources DOAJ
author Alp Karakoc
Ertugrul Taciroglu
spellingShingle Alp Karakoc
Ertugrul Taciroglu
Optimal automated path planning for infinitesimal and real-sized particle assemblies
AIMS Materials Science
Euclidean bipartite matching
cost flow
infinitesimal particle assembly
real-sized particle assembly
optical trapping
micro-manufacturing
microfluidics
regenerative medicine
author_facet Alp Karakoc
Ertugrul Taciroglu
author_sort Alp Karakoc
title Optimal automated path planning for infinitesimal and real-sized particle assemblies
title_short Optimal automated path planning for infinitesimal and real-sized particle assemblies
title_full Optimal automated path planning for infinitesimal and real-sized particle assemblies
title_fullStr Optimal automated path planning for infinitesimal and real-sized particle assemblies
title_full_unstemmed Optimal automated path planning for infinitesimal and real-sized particle assemblies
title_sort optimal automated path planning for infinitesimal and real-sized particle assemblies
publisher AIMS Press
series AIMS Materials Science
issn 2372-0484
publishDate 2017-07-01
description The present article introduces an algorithm for path planning and assembly of infinitesimal and real-sized particles by using a distance and path based permutation algorithm. The main objective is to define non-overlapping particle paths subject to minimal total path length during particles positioning and assembly. Thus, a local minimum is sought with a low computational cost. For this reason, an assignment problem, to be specific Euclidean bipartite matching problem, is presented, where the particles in the initial (random selection) and final (particle assembly) configurations are in one-to-one correspondence. The cost function for particle paths is defined through Euclidean distance of each particle between the initial and final configurations. Principally, a cost flow problem is formed and solved by determining an optimal permutation subject to the total Euclidean distance of the particles and their non-overlapping paths. Monte Carlo simulations are carried out for non-overlapping paths; thus, non-colliding particles, and then total path distances of the obtained sets are minimized, resulting in an optimal solution which may not be necessarily the global optimum. Case studies on basic and complex shaped infinitesimal and real-sized particle assemblies are shown with their total costs, i.e., path lengths. It is believed that the present study contributes to the current efforts in optical trapping automation for particle assemblies with possible applications, e.g., in the areas of micro-manufacturing, microfluidics, regenerative medicine and biotechnology.
topic Euclidean bipartite matching
cost flow
infinitesimal particle assembly
real-sized particle assembly
optical trapping
micro-manufacturing
microfluidics
regenerative medicine
url http://www.aimspress.com/Materials/article/1546/fulltext.html
work_keys_str_mv AT alpkarakoc optimalautomatedpathplanningforinfinitesimalandrealsizedparticleassemblies
AT ertugrultaciroglu optimalautomatedpathplanningforinfinitesimalandrealsizedparticleassemblies
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