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Üç Boyutlu Uzayda Çok Yöneticili Mobil Robotlar ile Sürü Hareketi Planlaması

Year 2019, Volume: 3 Issue: 2, 139 - 145, 23.12.2019

Abstract








Bu çalışmada üç boyutlu uzayda çok yöneticili bir yöntem ile mobil robotların ortak hareketi ve sürekli kalibrasyon ile
optimum konum belirlenmesi sağlanmıştır. Mobil robotların bilinen bir hedef görevi tamamlaması süresince izlemesi gereken
uygun yolların belirlenmesi üzerine çeşitli Sürü Zekası (SZ) algoritmaları incelenmiştir. İncelenen bu algoritmalardan yola
çıkılarak Örümcek Maymun Optimizasyon (ÖMO) algoritmasının hedeflenen optimum yol belirleme metodolojisine uygun
olduğu görülmüştür. Çoklu mobil robotların kullanım alanlarının genişlemesi ile belirlenen optimum yol boyunca senkronize
paralel hareketi sırasında birbirine göre konum belirlemesi ve mobil robotların birbiri ile sürekli iletişimde olması hedef görevin
başarılı şekilde tamamlanması açısından kritik hale gelmiştir. Bahsedilen kritik konulara çözüm geliştirmek amacıyla belirli bir
hedefe sahip ve belirli bir rota içerisinde senkronize paralel hareket eden çoklu mobil robotların üç boyutlu uzayda birbirine
göre konumunun matematiksel olarak modellenmesi ve bu konumdaki hata miktarının kalibre edilmesi üzerine çalışılmıştır.
Matematiksel olarak modellenen üç boyutlu konumdaki hata miktarının kalibrasyonu mobil robotların GPS ve erişim noktaları
gibi konumlandırıcı verileri ve sözde ters matriside (pseudo inverse matrix) kullanılarak hesaplanan konum bilgisi ile ifade
edilmiştir. Mobil robotların birbiri ile sürekli halde iletişimde olması ve birbirlerini belirlenen ortam sınırları içerisinde
kaybetmemeleri için alınan sinyal gücü göstergesi (RSSI- Received Signal Strength Indication) bilgilerinden yararlanılmıştır. 




References

  • [1] Beni, G., Wang, J., 1993. Swarm Intelligence in Cellular Robotic Systems, Robots and Biological Systems: Towards a New Bionics Springer, Berlin Heidelberg.
  • [2] Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
  • [3] Macro D. Ant colony system: a Cooperative learning approach to the trav- elling salesman problem. IEEE transaction on evolutionary computation 1997;1(1):53e66.
  • [4] Karaboga D. An idea based on honey bee swarm for numerical optimization. Erciyes university, engineering faculty, computer engineering department; 2005. Technical report-tr06.
  • [5] Yang XS. Nature-inspired metaheuristic algorithm. Luniver press; 2008.
  • [6] Passino KM. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 2002;22(3):52e67.
  • [7] Yang XS, Deb S. Cuckoo search via Levy flights. In: Nature and biologically inspired computing, NaBIC 2009, IEEE world congress; 2009. p. 210e4.
  • [8] Martínez-García FJ, Moreno-Perez JA. Jumping Frogs Optimization: a new swarm method for discrete optimization. Technical ReportDEIOC 3/2008. Spain: Universidad de La Laguna; 2008.
  • [9] Ahmed T. Sadiq Al-Obaidi, Hasanen S. Abdullah, and Zied O. Ahmed; " Meerkat Clan Algorithm: a New Swarm Intelligence Algorithm"; Indonesian Journal of Electrical Engineering and Computer Science; 2018.
  • [10] Bansal, J. C., Sharma, H., Jadon, S. S. & Clerc, M. (2014). Spider Monkey Optimization algorithm for numerical optimization. Memetic Computing, 6(1), pp. 31–47. doi: http://dx.doi.org/10.1007/s12293- 013-0128-0
  • [11] Z. Yang, Z. Zhou, and Y. Liu, “From RSSI to CSI: Indoor localization via channel response,” ACM Computing Surveys (CSUR), vol. 46, no. 2, p. 25, 2013.
  • [12] P. Castro, P. Chiu, T. Kremenek, and R. Muntz, “A probabilistic room location service for wireless networked environments,” in International Conference on Ubiquitous Computing, pp. 18–34, Springer, 2001.
  • [13] A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki, “Practical robust localization over large-scale 802.11 wireless networks,” in Proceedings of the 10th annual international conference on Mobile computing and networking, pp. 70–84, ACM, 2004.
  • [14] Zafari F, Gkelias A, Leung KK, 2019, A survey of indoor localization systems and technologies, Communications Surveys and Tutorials, ISSN: 1553-877X
  • [15] M. L. Rodrigues, L. F. Vieira, and M. Campos, “FingerprintingBased Radio Localization in Indoor Environments Using Multiple Wireless Technologies,” in IEEE 22nd Symp. Pers
  • [16] Understanding GPS : Principles and Applications by Elliott D. Kaplan, Artech House Publishers., 1996.
  • [17] Jodár, L.; Law, A. G.; Rezazadeh, A.; Watson, J. H.; and Wu, G. "Computations for the Moore-Penrose and Other Generalized Inverses." Congress. Numer. 80, 57-64, 1991.
  • [18] Rao, C. R. and Mitra, S. K. Generalized Inverse of Matrices and Its Applications. New York: Wiley, 1971.

Swarm Movement Planning with Multi Master Robots in 3D Space

Year 2019, Volume: 3 Issue: 2, 139 - 145, 23.12.2019

Abstract








In this study, it is provided to determine the optimum position with the joint movement of mobile robots and
continuous calibration with a multi-master method in 3D space. Various Swarm Intelligence (SI) algorithms have been examined
to determine the appropriate paths that mobile robots must follow during the completion of a known target task. Based on these
algorithms, it was found that the Spider Monkey Optimization (SMO) algorithm is in accordance with the targeted optimum path
methodology. The position of the mobile robots in relation to each other during synchronized parallel movement along the
optimum path determined and the uninterrupted communication of the mobile robots with each other have become critical for
the successful completion of the target task by the expansion of the usage areas of the multiple mobile robots. In order to solve
these critical issues, mathematical modeling and calibrating of the error position in three dimensional space of multiple mobile
robots which have a specific target and synchronized parallel movement in a given route have been studied. Calibration of the
error amount in 3-dimensional mathematically modeled position is expressed by the position data calculated according to using
pseudo inverse matrix and localization sources such as GPS and access points data of mobile robots. RSSI (Received Signal
Strength İndication) information was used to keep mobile robots in uninterrupted communication with each other and not to lose
each other within the specified environment. 




References

  • [1] Beni, G., Wang, J., 1993. Swarm Intelligence in Cellular Robotic Systems, Robots and Biological Systems: Towards a New Bionics Springer, Berlin Heidelberg.
  • [2] Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
  • [3] Macro D. Ant colony system: a Cooperative learning approach to the trav- elling salesman problem. IEEE transaction on evolutionary computation 1997;1(1):53e66.
  • [4] Karaboga D. An idea based on honey bee swarm for numerical optimization. Erciyes university, engineering faculty, computer engineering department; 2005. Technical report-tr06.
  • [5] Yang XS. Nature-inspired metaheuristic algorithm. Luniver press; 2008.
  • [6] Passino KM. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 2002;22(3):52e67.
  • [7] Yang XS, Deb S. Cuckoo search via Levy flights. In: Nature and biologically inspired computing, NaBIC 2009, IEEE world congress; 2009. p. 210e4.
  • [8] Martínez-García FJ, Moreno-Perez JA. Jumping Frogs Optimization: a new swarm method for discrete optimization. Technical ReportDEIOC 3/2008. Spain: Universidad de La Laguna; 2008.
  • [9] Ahmed T. Sadiq Al-Obaidi, Hasanen S. Abdullah, and Zied O. Ahmed; " Meerkat Clan Algorithm: a New Swarm Intelligence Algorithm"; Indonesian Journal of Electrical Engineering and Computer Science; 2018.
  • [10] Bansal, J. C., Sharma, H., Jadon, S. S. & Clerc, M. (2014). Spider Monkey Optimization algorithm for numerical optimization. Memetic Computing, 6(1), pp. 31–47. doi: http://dx.doi.org/10.1007/s12293- 013-0128-0
  • [11] Z. Yang, Z. Zhou, and Y. Liu, “From RSSI to CSI: Indoor localization via channel response,” ACM Computing Surveys (CSUR), vol. 46, no. 2, p. 25, 2013.
  • [12] P. Castro, P. Chiu, T. Kremenek, and R. Muntz, “A probabilistic room location service for wireless networked environments,” in International Conference on Ubiquitous Computing, pp. 18–34, Springer, 2001.
  • [13] A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki, “Practical robust localization over large-scale 802.11 wireless networks,” in Proceedings of the 10th annual international conference on Mobile computing and networking, pp. 70–84, ACM, 2004.
  • [14] Zafari F, Gkelias A, Leung KK, 2019, A survey of indoor localization systems and technologies, Communications Surveys and Tutorials, ISSN: 1553-877X
  • [15] M. L. Rodrigues, L. F. Vieira, and M. Campos, “FingerprintingBased Radio Localization in Indoor Environments Using Multiple Wireless Technologies,” in IEEE 22nd Symp. Pers
  • [16] Understanding GPS : Principles and Applications by Elliott D. Kaplan, Artech House Publishers., 1996.
  • [17] Jodár, L.; Law, A. G.; Rezazadeh, A.; Watson, J. H.; and Wu, G. "Computations for the Moore-Penrose and Other Generalized Inverses." Congress. Numer. 80, 57-64, 1991.
  • [18] Rao, C. R. and Mitra, S. K. Generalized Inverse of Matrices and Its Applications. New York: Wiley, 1971.
There are 18 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Zülal Tosunoğlu

Serkan Kurt

Publication Date December 23, 2019
Submission Date November 20, 2019
Published in Issue Year 2019 Volume: 3 Issue: 2

Cite

IEEE Z. Tosunoğlu and S. Kurt, “Üç Boyutlu Uzayda Çok Yöneticili Mobil Robotlar ile Sürü Hareketi Planlaması”, IJMSIT, vol. 3, no. 2, pp. 139–145, 2019.