Research Article
Altı Serbestlik Dereceli Bir Aydınlatma Manipülatörünün Yapay Sinir Ağları Temelli Ters Kinematik Çözümü ve Benzetimi
Abstract
Robot
manipülatörlerinde ters kinematik problem, manipülatörün uç işlevcisinin
istenilen konumda olması için eklem değişkenlerinin hesaplanmasını içeren
lineer olmayan bir problemdir. Ters kinematik problemin çözümü robotlarda
başlıca zorluklardandır. Bazı manipülatör konfigürasyonlarında bu problemim
çözümü zor da olsa mümkün olabilirken bazı konfigürasyonlarda mümkün
olamamaktadır. Bu çalışma 6 serbestlik dereceli (6-DOF) bir aydınlatma
robotunun ters kinematik probleminin yapay sinir ağları (YSA) temelli çözümünü
amaçlamaktadır. Aydınlatma robotu olarak ifade edilen bu robot, bir tıbbi
operasyon alanında istenilen bölgeyi aydınlatmak için kendiliğinden aydınlatma
görevini yapmaktadır. Aydınlatma için kullanılan manipülatör bilgisayar
destekli tasarım (CAD) programında tasarlanmış olup Simulink ortamına
aktarılmıştır. Bu sayede geliştirilen YSA modeli görsel olarak uygulaması
gerçekleştirilmiştir. Elde edilen sonuçlar grafiksel olarak verilmiştir. Elde
edilen sonuçlara göre, tasarlanan modelin makul sonuç verdiği gözlemlenmiştir.
References
- Alavandar, S., Nigam, M.J. 2008. Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators. Int. J. of Computers, Communications & Control, 3(2008), 224-234.
- Yildirim, Ş., Eski, İ. 2006. A QP Artificial Neural Network Inverse Kinematic Solution for Accurate Robot Path Control. Journal of Mechanical Science and Technology, 20(7), 917-928.
- Köker, R. 2013. “A Genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization” Information Sciences 222(2013), 528-543.
- Köker, R. 2011. “A neuro-genetic approach to the inverse kinematics solution of robotic manipulators” Scientific Research and Essays, 6 (13), 2784-2794.
- Duka, A.V. 2014. Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technology, 12(2014), 20 – 27.
- Bingül Z., Karahan, O. 2011. A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. 38(1), 1017–1031.
- Yahya, S., Moghavvemi, M. 2014. Artificial neural networks aided solution to the problem of geometrically bounded singularities and joint limits prevention of a three dimensional planar redundant manipülatör. Neuro Computing 137(2014), 34-36.
- Küçük, S., Bingül, Z. 2014. Inverse kinematic solutions for industrial robot manipulators with offset wrists. Applied Mathematical Modeling, 38(2014), 1983-1999.
- Luv, A., Kush, A., Ruth. J. 2014. Use of artificial neural networks for the development of an inverse kinematic solution and visual identification of singularity zone(s) Procedia CIRP, 17(2014), 812 – 817.
- Mashhadany, Y. I. 2012. ANFIS-Inverse-Controlled PUMA 560 Workspace Robot with Spherical Wrist. Procedia Engineering, 41(2012), 700 – 709.
- Köker, Öz, R. C., Çakar, T., Ekiz, H. 2004. A study of neural network based inverse kinematics solution for a three-joint robot. Robotics and Autonomous Systems, 49(2004), 227–234. Chaudhary, H., Prasad, R., Sukavanum, N. 2012. Position analysis based approach for trajectory tracking control of scorbot-er-v plus robot manipülatör. International Journal of Advances in Engineering & Technology, 3(2), 253-264.
- Jha P., Biswal, BB., 2014. A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator. International Journal of Robotics and Automation (IJRA), 3(1), 52-61.
- Almusawi, ARJ., Dülger, LC., Kapucu, S. 2016 A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242). Research Article Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Volume 2016, Article 10 pages.
- Nanda S.K., Panda, S., Subudhi S.P., Das K.R., 2012. A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators. I.J. Intelligent Systems and Applications, 9(2012), 81-91.
- Yin F., Wang Y.N., Wei, S.N. 2011. Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms. Acta Automatica Sinica, 37(1), 74-82.
- Hoang, N-B., Kang H-J. 2016. Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force. Neuro computing, 188(2016), 12-22.
- Karaatlı, M., Albeni, M. 2011. Forecasting rose flower planting areas using artificial neural networks. Akdeniz University International Journal of Alanya Faculty of Busıness, 3(2), 137-149.
- Karlık, B., Cemel, S. 2012. Diagnosing diabetes from breath odor using artificial neural networks” Turkiye Klinikleri J Med Sci 32(2):331-336.
- Çakır, Ş., Ertunç, H.M., Ocak, H. 2009. A Case Study for Identification of Texture in Carbonate Rocks Using Artificial Neural Networks: Akveren Formation. Uygulamalı Yerbilimleri 2(2009), 71-79.
- Beale, M.H., Hagan, M.T., Demuth, H.B. 2017. PDF Documentation for Neural Network Toolbox.https://www.mathworks.com/help/pdf_doc/nnet/index.html?s_cid=doc_ftr (Erişim Tarihi: 10.04.2017).
Year 2018,
Volume: 6 Issue: 1, 117 - 125, 30.03.2018
Abstract
References
- Alavandar, S., Nigam, M.J. 2008. Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators. Int. J. of Computers, Communications & Control, 3(2008), 224-234.
- Yildirim, Ş., Eski, İ. 2006. A QP Artificial Neural Network Inverse Kinematic Solution for Accurate Robot Path Control. Journal of Mechanical Science and Technology, 20(7), 917-928.
- Köker, R. 2013. “A Genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization” Information Sciences 222(2013), 528-543.
- Köker, R. 2011. “A neuro-genetic approach to the inverse kinematics solution of robotic manipulators” Scientific Research and Essays, 6 (13), 2784-2794.
- Duka, A.V. 2014. Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technology, 12(2014), 20 – 27.
- Bingül Z., Karahan, O. 2011. A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. 38(1), 1017–1031.
- Yahya, S., Moghavvemi, M. 2014. Artificial neural networks aided solution to the problem of geometrically bounded singularities and joint limits prevention of a three dimensional planar redundant manipülatör. Neuro Computing 137(2014), 34-36.
- Küçük, S., Bingül, Z. 2014. Inverse kinematic solutions for industrial robot manipulators with offset wrists. Applied Mathematical Modeling, 38(2014), 1983-1999.
- Luv, A., Kush, A., Ruth. J. 2014. Use of artificial neural networks for the development of an inverse kinematic solution and visual identification of singularity zone(s) Procedia CIRP, 17(2014), 812 – 817.
- Mashhadany, Y. I. 2012. ANFIS-Inverse-Controlled PUMA 560 Workspace Robot with Spherical Wrist. Procedia Engineering, 41(2012), 700 – 709.
- Köker, Öz, R. C., Çakar, T., Ekiz, H. 2004. A study of neural network based inverse kinematics solution for a three-joint robot. Robotics and Autonomous Systems, 49(2004), 227–234. Chaudhary, H., Prasad, R., Sukavanum, N. 2012. Position analysis based approach for trajectory tracking control of scorbot-er-v plus robot manipülatör. International Journal of Advances in Engineering & Technology, 3(2), 253-264.
- Jha P., Biswal, BB., 2014. A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator. International Journal of Robotics and Automation (IJRA), 3(1), 52-61.
- Almusawi, ARJ., Dülger, LC., Kapucu, S. 2016 A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242). Research Article Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Volume 2016, Article 10 pages.
- Nanda S.K., Panda, S., Subudhi S.P., Das K.R., 2012. A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators. I.J. Intelligent Systems and Applications, 9(2012), 81-91.
- Yin F., Wang Y.N., Wei, S.N. 2011. Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms. Acta Automatica Sinica, 37(1), 74-82.
- Hoang, N-B., Kang H-J. 2016. Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force. Neuro computing, 188(2016), 12-22.
- Karaatlı, M., Albeni, M. 2011. Forecasting rose flower planting areas using artificial neural networks. Akdeniz University International Journal of Alanya Faculty of Busıness, 3(2), 137-149.
- Karlık, B., Cemel, S. 2012. Diagnosing diabetes from breath odor using artificial neural networks” Turkiye Klinikleri J Med Sci 32(2):331-336.
- Çakır, Ş., Ertunç, H.M., Ocak, H. 2009. A Case Study for Identification of Texture in Carbonate Rocks Using Artificial Neural Networks: Akveren Formation. Uygulamalı Yerbilimleri 2(2009), 71-79.
- Beale, M.H., Hagan, M.T., Demuth, H.B. 2017. PDF Documentation for Neural Network Toolbox.https://www.mathworks.com/help/pdf_doc/nnet/index.html?s_cid=doc_ftr (Erişim Tarihi: 10.04.2017).
There are 20 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Tasarım ve Teknoloji |
| Authors | |
| Publication Date | March 30, 2018 |
| Submission Date | July 14, 2017 |
| Published in Issue | Year 2018 Volume: 6 Issue: 1 |
