Research article Topical Sections

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

  • Received: 31 May 2017 Accepted: 12 July 2017 Published: 28 July 2017
  • 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.

    Citation: Alp Karakoc, Ertugrul Taciroglu. Optimal automated path planning for infinitesimal and real-sized particle assemblies[J]. AIMS Materials Science, 2017, 4(4): 847-855. doi: 10.3934/matersci.2017.4.847

    Related Papers:

    [1] Christopher M. Kribs-Zaleta . Sociological phenomena as multiple nonlinearities: MTBI's new metaphor for complex human interactions. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1587-1607. doi: 10.3934/mbe.2013.10.1587
    [2] Abba B. Gumel, Baojun Song . Existence of multiple-stable equilibria for a multi-drug-resistant model of mycobacterium tuberculosis. Mathematical Biosciences and Engineering, 2008, 5(3): 437-455. doi: 10.3934/mbe.2008.5.437
    [3] Yuyi Xue, Yanni Xiao . Analysis of a multiscale HIV-1 model coupling within-host viral dynamics and between-host transmission dynamics. Mathematical Biosciences and Engineering, 2020, 17(6): 6720-6736. doi: 10.3934/mbe.2020350
    [4] Rachel Clipp, Brooke Steele . An evaluation of dynamic outlet boundary conditions in a 1D fluid dynamics model. Mathematical Biosciences and Engineering, 2012, 9(1): 61-74. doi: 10.3934/mbe.2012.9.61
    [5] Bertin Hoffmann, Udo Schumacher, Gero Wedemann . Absence of convection in solid tumors caused by raised interstitial fluid pressure severely limits success of chemotherapy—a numerical study in cancers. Mathematical Biosciences and Engineering, 2020, 17(5): 6128-6148. doi: 10.3934/mbe.2020325
    [6] John Boscoh H. Njagarah, Farai Nyabadza . Modelling the role of drug barons on the prevalence of drug epidemics. Mathematical Biosciences and Engineering, 2013, 10(3): 843-860. doi: 10.3934/mbe.2013.10.843
    [7] Yubo Sun, Yuanyuan Cheng, Yugen You, Yue Wang, Zhizhong Zhu, Yang Yu, Jianda Han, Jialing Wu, Ningbo Yu . A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease. Mathematical Biosciences and Engineering, 2023, 20(8): 13474-13490. doi: 10.3934/mbe.2023601
    [8] Mingzhu Qu, Chunrui Zhang, Xingjian Wang . Analysis of dynamic properties on forest restoration-population pressure model. Mathematical Biosciences and Engineering, 2020, 17(4): 3567-3581. doi: 10.3934/mbe.2020201
    [9] Salih Djillali, Soufiane Bentout, Tarik Mohammed Touaoula, Abdessamad Tridane . Global dynamics of alcoholism epidemic model with distributed delays. Mathematical Biosciences and Engineering, 2021, 18(6): 8245-8256. doi: 10.3934/mbe.2021409
    [10] Tianqi Song, Chuncheng Wang, Boping Tian . Mathematical models for within-host competition of malaria parasites. Mathematical Biosciences and Engineering, 2019, 16(6): 6623-6653. doi: 10.3934/mbe.2019330
  • 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.


    [1] Ghadiri R, Weigel T, Esen C, et al. (2012) Microassembly of complex and three-dimensional microstructures using holographic optical tweezers. J Micromech Microeng 22: 065016. doi: 10.1088/0960-1317/22/6/065016
    [2] Haghighi R, Cheah CC (2014) Multi-cell formation following in a concurrent control framework. 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), 499–504.
    [3] Shaw LA, Chizari S, Panas RM, et al. (2016) Holographic optical assembly and photopolymerized joining of planar microspheres. Opt Lett 41: 3571–3574. doi: 10.1364/OL.41.003571
    [4] Cizmar T, Romero L, Dholakia K, et al. (2010) Multiple optical trapping and binding: new routes to self-assembly. J Phys B-At Mol Opt 43: 102001. doi: 10.1088/0953-4075/43/10/102001
    [5] Roux R, Ladavière C, Montembault A, et al. (2013) Particle assemblies: Toward new tools for regenerative medicine. Mater Sci Eng C 33: 997–1007. doi: 10.1016/j.msec.2012.12.002
    [6] Svoboda K, Block SM (1994) Force and velocity measured for single kinesin molecules. Cell 77: 773–784. doi: 10.1016/0092-8674(94)90060-4
    [7] Padgett M, Di Leonardo R (2011) Holographic optical tweezers and their relevance to lab on chip devices. Lab Chip 11: 1196–1205. doi: 10.1039/c0lc00526f
    [8] Kirkham GR, Britchford E, Upton T, et al. (2015) Precision Assembly of Complex Cellular Microenvironments using Holographic Optical Tweezers. Sci Rep 5: 8577. doi: 10.1038/srep08577
    [9] Chapin SC, Germain V, Dufresne ER (2006) Automated trapping, assembly, and sorting with holographic optical tweezers. Opt Express 14: 13095–13100. doi: 10.1364/OE.14.013095
    [10] Ashkin A, Dziedzic JM, Bjorkholm JE, et al. (1986) Observation of a single-beam gradient force optical trap for dielectric particles. Opt Lett 11: 288–290. doi: 10.1364/OL.11.000288
    [11] Bowman RW, Padgett MJ (2013) Optical trapping and binding. Rep Prog Phys 76: 026401. doi: 10.1088/0034-4885/76/2/026401
    [12] Skala J, Kolingerova I, Hyka J (2009) A Monte Carlo solution to the minimal Euclidean matching. Algoritmy 402–411.
    [13] Rendl F (1988) On the Euclidean assignment problem. J Comput Appl Math 23: 257–265. doi: 10.1016/0377-0427(88)90001-5
    [14] Caracciolo S, Lucibello C, Parisi G, et al. (2014) Scaling hypothesis for the Euclidean bipartite matching problem. Phys Rev E 90: 012118.
    [15] Karakoc A, Freund J (2013) Statistical strength analysis for honeycomb materials. Int J Appl Mech 5: 1350021. doi: 10.1142/S175882511350021X
    [16] Mathematica. Available from: https://reference.wolfram.com/language/ref/MorphologicalComponents.html.
    [17] Karakoc A, Sjolund J, Reza M, et al. (2016) Modeling of wood-like cellular materials with a geometrical data extraction algorithm. Mech Mater 93: 209–219. doi: 10.1016/j.mechmat.2015.10.019
  • This article has been cited by:

    1. Diana M. Thomas, Marion Weedermann, Bernard F. Fuemmeler, Corby K. Martin, Nikhil V. Dhurandhar, Carl Bredlau, Steven B. Heymsfield, Eric Ravussin, Claude Bouchard, Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends, 2014, 22, 19307381, 590, 10.1002/oby.20520
    2. Paul J. Gruenewald, William R. Ponicki, Lillian G. Remer, Fred W. Johnson, Lance A. Waller, Dennis M. Gorman, Li Zhu, 2013, Chapter 9, 978-90-481-8920-5, 167, 10.1007/978-90-481-8921-2_9
    3. D. M. G. Comissiong, J. Sooknanan, A review of the use of optimal control in social models, 2018, 6, 2195-268X, 1841, 10.1007/s40435-018-0405-3
    4. Paul J. Gruenewald, William R. Ponicki, Lillian G. Remer, Lance A. Waller, Li Zhu, Dennis M. Gorman, Mapping the Spread of Methamphetamine Abuse in California From 1995 to 2008, 2013, 103, 0090-0036, 1262, 10.2105/AJPH.2012.300779
    5. L.H.A. Monteiro, Population dynamics in educational institutions considering the student satisfaction, 2016, 30, 10075704, 236, 10.1016/j.cnsns.2015.06.015
    6. Fabio Sánchez, Xiaohong Wang, Carlos Castillo-Chávez, Dennis M. Gorman, Paul J. Gruenewald, 2007, 9780123694294, 353, 10.1016/B978-012369429-4/50046-X
    7. Baojun Song, Zhilan Feng, Gerardo Chowell, From the guest editors, 2013, 10, 1551-0018, 10.3934/mbe.2013.10.5i
    8. Fabio Sanchez, Juan G. Calvo, Esteban Segura, Zhilan Feng, A partial differential equation model with age-structure and nonlinear recidivism: Conditions for a backward bifurcation and a general numerical implementation, 2019, 78, 08981221, 3916, 10.1016/j.camwa.2019.06.021
    9. David Greenhalgh, Martin Griffiths, Dynamic phenomena arising from an extended Core Group model, 2009, 221, 00255564, 136, 10.1016/j.mbs.2009.08.003
    10. Daniel M. Romero, Christopher M. Kribs-Zaleta, Anuj Mubayi, Clara Orbe, An epidemiological approach to the spread of political third parties, 2011, 15, 1553-524X, 707, 10.3934/dcdsb.2011.15.707
    11. Francisco-José Santonja, Iván-C. Lombana, María Rubio, Emilio Sánchez, Javier Villanueva, A network model for the short-term prediction of the evolution of cocaine consumption in Spain, 2010, 52, 08957177, 1023, 10.1016/j.mcm.2010.02.032
    12. Aqsa Nazir, Naveed Ahmed, Umar Khan, Syed Tauseef Mohyud-Din, A conformable mathematical model for alcohol consumption in Spain, 2019, 12, 1793-5245, 1950057, 10.1142/S1793524519500578
    13. Anuj Mubayi, Priscilla E. Greenwood, Carlos Castillo-Chávez, Paul J. Gruenewald, Dennis M. Gorman, The impact of relative residence times on the distribution of heavy drinkers in highly distinct environments, 2010, 44, 00380121, 45, 10.1016/j.seps.2009.02.002
    14. Bechir Amdouni, Marlio Paredes, Christopher Kribs, Anuj Mubayi, Why do students quit school? Implications from a dynamical modelling study, 2017, 473, 1364-5021, 20160204, 10.1098/rspa.2016.0204
    15. Yun Kang, Carlos Castillo-Chávez, A simple epidemiological model for populations in the wild with Allee effects and disease-modified fitness, 2014, 19, 1553-524X, 89, 10.3934/dcdsb.2014.19.89
    16. Stephen Wirkus, Christopher Kribs-Zaleta, Erika T. Camacho, The mathematical and theoretical biology institute - a model of mentorship through research, 2013, 10, 1551-0018, 1351, 10.3934/mbe.2013.10.1351
    17. Francisco-José Santonja, Emilio Sánchez, María Rubio, José-Luis Morera, Alcohol consumption in Spain and its economic cost: A mathematical modeling approach, 2010, 52, 08957177, 999, 10.1016/j.mcm.2010.02.029
    18. Emilio Sánchez, Rafael-Jacinto Villanueva, Francisco-José Santonja, María Rubio, Predicting cocaine consumption in Spain: A mathematical modelling approach, 2011, 18, 0968-7637, 108, 10.3109/09687630903443299
    19. Daniel M. Romero, Christopher M. Kribs-Zaleta, Anuj Mubayi, Clara Orbe, An Epidemiological Approach to the Spread of Political Third Parties, 2009, 1556-5068, 10.2139/ssrn.1503124
    20. Sunhwa Choi, Jonggul Lee, Eunok Jung, OPTIMAL STRATEGIES FOR PREVENTION OF ECSTASY USE, 2014, 18, 1226-9433, 1, 10.12941/jksiam.2014.18.001
    21. J. Sooknanan, D.M.G. Comissiong, A mathematical model for the treatment of delinquent behaviour, 2018, 63, 00380121, 60, 10.1016/j.seps.2017.08.001
    22. Joanna Sooknanan, Donna M. G. Comissiong, When behaviour turns contagious: the use of deterministic epidemiological models in modeling social contagion phenomena, 2017, 5, 2195-268X, 1046, 10.1007/s40435-016-0271-9
    23. Christopher Kribs-Zaleta, Sociological phenomena as multiple nonlinearities: MTBI's new metaphor for complex human interactions, 2013, 10, 1551-0018, 1587, 10.3934/mbe.2013.10.1587
    24. Ariel Cintrón-Arias, Fabio Sánchez, Xiaohong Wang, Carlos Castillo-Chavez, Dennis M. Gorman, Paul J. Gruenewald, 2009, Chapter 14, 978-90-481-2312-4, 343, 10.1007/978-90-481-2313-1_14
    25. Caroline E. Walters, Jeremy R. Kendal, An SIS model for cultural trait transmission with conformity bias, 2013, 90, 00405809, 56, 10.1016/j.tpb.2013.09.010
    26. Yusra Bibi Ruhomally, Muhammad Zaid Dauhoo, Laurent Dumas, A graph cellular automaton with relation-based neighbourhood describing the impact of peer influence on the consumption of marijuana among college-aged youths, 2021, 0, 2164-6074, 0, 10.3934/jdg.2021011
    27. Theophilus Kwofie, Matthias Dogbatsey, Stephen E. Moore, Curtailing crime dynamics: A mathematical approach, 2023, 8, 2297-4687, 10.3389/fams.2022.1086745
    28. Baojun Song, Basic reinfection number and backward bifurcation, 2021, 18, 1551-0018, 8064, 10.3934/mbe.2021400
    29. Cesar Montalvo-Clavijo, Carlos Castillo-Chavez, Charles Perrings, Anuj Mubayi, Neighborhood effects, college education, and social mobility, 2022, 00380121, 101471, 10.1016/j.seps.2022.101471
    30. Christopher Anaya, Clara Burgos, Juan-Carlos Cortés, Rafael-J. Villanueva, Capturing the Data Uncertainty Change in the Cocaine Consumption in Spain Using an Epidemiologically Based Model, 2016, 2016, 1085-3375, 1, 10.1155/2016/1758459
    31. Julia Calatayud, Marc Jornet, Jorge Mateu, Spatial modeling of crime dynamics: Patch and reaction–diffusion compartmental systems, 2023, 0170-4214, 10.1002/mma.9064
    32. Fabio Sanchez, Jorge Arroyo-Esquivel, Juan G. Calvo, A mathematical model with nonlinear relapse: conditions for a forward-backward bifurcation, 2023, 17, 1751-3758, 10.1080/17513758.2023.2192238
    33. Yusra Bibi Ruhomally, Muhammad Zaid Dauhoo, 2023, 9789815079241, 15, 10.2174/9789815079241123010005
    34. Julia Calatayud, Marc Jornet, Jorge Mateu, A dynamical mathematical model for crime evolution based on a compartmental system with interactions, 2024, 0020-7160, 1, 10.1080/00207160.2024.2302840
    35. Leigh B. Pearcy, Suzanne Lenhart, W. Christopher Strickland, Structural instability and linear allocation control in generalized models of substance use disorder, 2024, 371, 00255564, 109169, 10.1016/j.mbs.2024.109169
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4754) PDF downloads(968) Cited by(5)

Article outline

Figures and Tables

Figures(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog