Experimental Validation for Inverse Kinematics of a Five Axis Robotic Manipulator Using Deep Artificial Neural Network
Shubham Kamlesh Shah1, Ruby Mishra2
1Shubham Kamlesh Shah, is Currently Pursuing PhD Program in Mechanical Engineering at KIIT Deemed to be University, India.
2Ruby Mishra*, Currently an Associate Professor in Mechanical Engineering at KIIT Deemed to be University, India.
Manuscript received on October 16, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 3943-3946 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5025119119/2019©BEIESP | DOI: 10.35940/ijitee.A5025.119119
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Abstract: The use of robotics is to improvise and simplify human life. Robotic manipulators have been around for a while now and are being used in many different sectors such as industries, households, warehouses, medicine etc. Solving of inverse kinematics is one of the most complex issues faced while designing the robotic manipulator. In this research a Deep Artificial neural network (D-ANN) model is proposed to solve inverse kinematics of a 5-axis robotic manipulator with rotary joints. The D-ANN model is trained in MATLAB. Training dataset was generated using forward kinematics equations obtained easily from transformation matrix of the robotic manipulator. To validate predictions made by this model an experimental robotic arm manipulator Is fabricated. A smart camera setup has been linked to MATLAB for real time image processing and calculating the deviation of the end needle in reaching the desired target coordinate. The trained model yielded satisfactory results with ±0.03 radians error and this was also validated experimentally. This research will help the robotic manipulator reach the desired target coordinates even when one does not have enough input data.Paper Setup must be in A4 size with Margin: Top 0.7”, Bottom 0.7”, Left 0.65”, 0.65”, Gutter 0”, and Gutter Position Top. Paper must be in two Columns after Authors Name with Width 8.27”, height 11.69” Spacing 0.2”. Whole paper must be with: Font Name Times New Roman, Font Size 10, Line Spacing 1.05 EXCEPT Abstract, Keywords (Index Term), Paper Tile, References, Author Profile (in the last page of the paper, maximum 400 words), All Headings, and Manuscript Details (First Page, Bottom, left side).
Keywords: Deep Artificial Neural Network (D-ANN), Image Processing, Inverse Kinematics, MATLAB, Robotic Manipulator.
Scope of the Article: Neural Information Processing