Yıl 2018, Cilt 6, Sayı 2, Sayfalar 124 - 133 2018-08-03

Designing an Object Tracker Self-Balancing Robot
Designing an Object Tracker Self-Balancing Robot

Yunus Celik [1] , Mahit Güneş [2]

14 19

Real-time robots are quite common in our daily life. These robots are working as a part of the process in industry or a medical assistance in hospitals to serve humanity. Designing the robots according to the desired referent and making the given tasks with high accuracy makes them more and more popular in these days. In this work, the designed two-wheeled balancing robots with integrated camera track object autonomously. This work has two important stages. The first stage is about balancing the robot with the angle information taken from IMU sensor and implementation of PID control. IMU sensors create lots of noisy signals because of its natural structures. Kalman filter was used to denoise these noisy signals to have a smooth signal for a better balance control. The second stage is about image processing and objects recognition. This section was completed by using Matlab Image Processing Toolbox which can be used Arduino microcontroller board synchronously. In this section, algorithm infers motion information of objects. Motors were controlled according to motion information of moving objects. In the end, an object tracker self-balance robot was constructed. Balance control of the robot was managed by PID controller and accelerometer signals were denoised by a Kalman Filter. It was clarified that using PID controller and Kalman Filter together have a positive effect to balance the robot on the desired angle.

Real-time robots are quite common in our daily life. These robots are working as a part of the process in industry or a medical assistance in hospitals to serve humanity. Designing the robots according to the desired referent and making the given tasks with high accuracy makes them more and more popular in these days. In this work, the designed two-wheeled balancing robots with integrated camera track object autonomously. This work has two important stages. The first stage is about balancing the robot with the angle information taken from IMU sensor and implementation of PID control. IMU sensors create lots of noisy signals because of its natural structures. Kalman filter was used to denoise these noisy signals to have a smooth signal for a better balance control. The second stage is about image processing and objects recognition. This section was completed by using Matlab Image Processing Toolbox which can be used Arduino microcontroller board synchronously. In this section, algorithm infers motion information of objects. Motors were controlled according to motion information of moving objects. In the end, an object tracker self-balance robot was constructed. Balance control of the robot was managed by PID controller and accelerometer signals were denoised by a Kalman Filter. It was clarified that using PID controller and Kalman Filter together have a positive effect to balance the robot on the desired angle.

  • [1] Y. Ding, J. Gafford, And M. Kunio, "Modeling, Simulation And Fabrication Of A Balancing Robot," Harvard University, Massachusettes Institute Of Technology, Vol. 151, 2012.
  • [2] A. Fakharian, T. Gustafsson, And M. Mehrfam, "Adaptive Kalman Filtering Based Navigation: An Imu/Gps Integration Approach," In Networking, Sensing And Control (Icnsc), 2011 Ieee International Conference On, 2011, Pp. 181-185: Ieee.
  • [3] R.C.Ooi,"Balancing A Two-Wheeled Autonomous Robot," University Of Western Australia, Vol. 3, 2003.
  • [4] A. Castro, "Modeling And Dynamic Analysis Of A Two-Wheeled Inverted-Pendulum," Georgia Institute Of Technology, 2012.
  • [5] J. L. C. Miranda, "Application Of Kalman Filtering And Pid Control For Direct Inverted Pendelum Control," 2010.
  • [6] C. Sundin And F. Thorstenson, "Autonomous Balancing Robot: Design And Construction Of A Balancing Robot," Ed: Master Of Science Thesis. Chalmers University Of Technology. Goteborg, Sweden, 2012.
  • [7] S. Balasubramanian And M. N. Lathiff, "Self Balancing Robot," 2011.
  • [8] M. A. Şen, "İki Tekerlekli Robot Için Bulanık Mantık Tabanlı Kontrolcü Tasarımı Ve Arı Algoritması Kullanarak Optimizasyonu," Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2014.
  • [9] A. Gani, E. Kılıç, Ö. F. Keçecioğlu, H. Açıkgöz, And M. Şekkeli, "Endüstriyel Uygulamalarda Kullanilan Karişim Tankinin Seviye Ve Sicaklik Denetimi İçin Pid Ve Bulanik Mantik Denetleyici Tasarimi," Engineer & The Machinery Magazine, No. 675, 2016.
  • [10 ]H. Açıkgöz, Ö. F. Keçecioğlu, M. Güneş, And M. Şekkeli, "Öz Ayarlamalı Bulanık-Pid Denetleyici İle Hidrolik Türbinin Benzetim Çalışması," Academic Platform-Journal Of Engineering And Science, Vol. 3, No. 1, Pp. 7-15, 2015.
  • [11] A. Gani, O. F. Kececioglu, H. Acikgoz, And M. Sekkeli, "Fuzzy Logic Controller Design Based On Sugeno Inference Method For Nonlinear Inverted Pendulum Dynamical System," Sigma Journal Of Engineering And Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisi, Vol. 8, No. 1, Pp. 19-30, 2017.
  • [12] S. A. Junoh, "Two-Wheeled Balancing Robot Controller Designed Using Pid," Universiti Tun Hussein Onn Malaysia, 2015.
  • [13] A. Ünlütürk, U. Güner, And Ö. Aydoğdu, "Yeni Bir Pi-V Tipi Denetleyici Tasarimi Ve Denge Robotu Üzerinde Uygulanmasi."
  • [14] H.-C. Sung, "Balancing Robot Control And Implementation," 2015.
  • [15] H. Guducu, "Building Detection From Satellite Images Using Shadow And Color Information," Msc Thesis, 2008.
  • [16] O. Enginoğlu, "Design And Control Of Balancing Robot," Deü Fen Bilimleri Enstitüsü, 2012.
  • [17] M. S. Uzuner, N. Yilmaz, And M. Bayrak, "Görme Tabanli Mobil Robot İle Farkli Renklerde Nesnelerin Gerçek Zamanli Takibi," Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, Vol. 25, No. 4, 2010.
  • [18] S. Dutta And B. B. Chaudhuri, "A Color Edge Detection Algorithm In Rgb Color Space," In Advances In Recent Technologies In Communication And Computing, 2009. Artcom'09. International Conference On, 2009, Pp. 337-340: IEEE.
  • [19] V. Kravtchenko, "Tracking Color Objects In Real Time," University Of British Columbia, 1999.
  • [20] T. Şentürk, "Hareketli Kamera Ile Gerçek Zamanlı Hareketli Nesne Takibi," Ytü Fen Bilimleri Enstitüsü, 2008.
  • [21] H. Takemura, K. Ito, And H. Mizoguchi, "Person Following Mobile Robot Under Varying Illumination Based On Distance And Color Information," In Robotics And Biomimetics, 2007. Robio 2007. Ieee International Conference On, 2007, Pp. 1500-1505: Ieee.
  • [22] M. S. Uzer, "Görme Tabanlı Mobil Robotun Farklı Renklerde Nesneleri Takibi," Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2008.
  • [23] B. Karasulu, "Review And Evaluation Of Well-Known Methods For Moving Object Detection And Tracking In Videos," Journal Of Aeronautics And Space Technologies, Vol. 4, No. 4, Pp. 11-22, 2010.
  • [24] T. Gevers And A. W. Smeulders, "Color-Based Object Recognition," Pattern Recognition, Vol. 32, No. 3, Pp. 453-464, 1999.
  • [25] Y. Çelik, M. Altun, And M. Güneş, "Color Based Moving Object Tracking With An Active Camera Using Motion Information," In Artificial Intelligence And Data Processing Symposium (Idap), 2017 International, 2017, Pp. 1-4: Ieee.
  • [26] M. F. Aslan, A. Durdu, And K. Sabanci, "Shopping Robot That Make Real Time Color Tracking Using Image Processing Techniques," International Journal Of Applied Mathematics, Electronics And Computers, Vol. 5, No. 3, Pp. 62-66, 2017.
  • [27] W. Ladys Law Skarbek And A. Koschan, "Colour Image Segmentation A Survey," Ieee Transactions On Circuits And Systems For Video Technol-Ogy, Vol. 14, 1994.
  • [28] M. M. Kelek50, M. F. Aslan, And A. Durdu, "Real-Time Target Tracking Using Fast Object Detection."
  • [29] https://Visaya.Solutions/Article/Pid-Controller-Small-Quad-Copter-Drones.Accesed:03.04.2018.10:40 Ol.151,2012.
Birincil Dil en
Konular Mühendislik ve Temel Bilimler
Yayımlanma Tarihi Mayıs
Dergi Bölümü Makaleler
Yazarlar

Yazar: Yunus Celik
Kurum: Karamanoğlu Mehmetbey Üniversitesi | Karamanoğlu Mehmetbey University
Ülke: Turkey


Yazar: Mahit Güneş (Sorumlu Yazar)
Kurum: Kahramanmaraş Sütçü İmam Üniversitesi
Ülke: Turkey


Bibtex @araştırma makalesi { apjes414715, journal = {Akademik Platform Mühendislik ve Fen Bilimleri Dergisi}, issn = {}, eissn = {2147-4575}, address = {Akademik Platform}, year = {2018}, volume = {6}, pages = {124 - 133}, doi = {10.21541/apjes.414715}, title = {Designing an Object Tracker Self-Balancing Robot}, key = {cite}, author = {Celik, Yunus and Güneş, Mahit} }
APA Celik, Y , Güneş, M . (2018). Designing an Object Tracker Self-Balancing Robot. Akademik Platform Mühendislik ve Fen Bilimleri Dergisi, 6 (2), 124-133. DOI: 10.21541/apjes.414715
MLA Celik, Y , Güneş, M . "Designing an Object Tracker Self-Balancing Robot". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 6 (2018): 124-133 <http://dergipark.gov.tr/apjes/issue/38735/414715>
Chicago Celik, Y , Güneş, M . "Designing an Object Tracker Self-Balancing Robot". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 6 (2018): 124-133
RIS TY - JOUR T1 - Designing an Object Tracker Self-Balancing Robot AU - Yunus Celik , Mahit Güneş Y1 - 2018 PY - 2018 N1 - doi: 10.21541/apjes.414715 DO - 10.21541/apjes.414715 T2 - Akademik Platform Mühendislik ve Fen Bilimleri Dergisi JF - Journal JO - JOR SP - 124 EP - 133 VL - 6 IS - 2 SN - -2147-4575 M3 - doi: 10.21541/apjes.414715 UR - http://dx.doi.org/10.21541/apjes.414715 Y2 - 2018 ER -
EndNote %0 Akademik Platform Mühendislik ve Fen Bilimleri Dergisi Designing an Object Tracker Self-Balancing Robot %A Yunus Celik , Mahit Güneş %T Designing an Object Tracker Self-Balancing Robot %D 2018 %J Akademik Platform Mühendislik ve Fen Bilimleri Dergisi %P -2147-4575 %V 6 %N 2 %R doi: 10.21541/apjes.414715 %U 10.21541/apjes.414715
ISNAD Celik, Yunus , Güneş, Mahit . "Designing an Object Tracker Self-Balancing Robot". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 6 / 2 (Ağustos 2018): 124-133. http://dx.doi.org/10.21541/apjes.414715