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Machine Learning-Based Estimation of End Effector Position in Three-Dimension Robotic Workspace

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  • Hamna Baig, Ejaz Ahmed, Ihtesham Jadoon

    (Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock, Pakistan. Department of Electrical Engineering Aero Space and Aviation, Air University Islamabad KamraCampusAttockPakistan))

Abstract

Introduction/Importance of Study:The Workspace is the area around the robot where a robot can freely move with possible input variations of different joint angles. Novelty statement:Conventionally iterative simulation methods are used to find robotic workspace. Which are computationally slow and difficult to model. Our approach utilizes machine-learning algorithms to predict the workspace and position of an end effector. Material and Method:Multiple Linear Regression (MLR), Decision-Tree Regression,and Artificial Neural Network (ANN) algorithms trained for prediction. The dataset, which is collected and used as train and test data, is further for the validation step.Result and Discussion:By simulating the robot with the Denavit-Hartenberg (D-H)approach in MATLAB.The results findings show the accuracy of Machine learning algorithms specifically Artificial Neural Networks(ANN) perform betterthan conventional mathematical methodsConcluding Remarks:Artificial Neural Network (ANN) outperformed other machine learning methods.

Suggested Citation

  • Hamna Baig, Ejaz Ahmed, Ihtesham Jadoon, 2024. "Machine Learning-Based Estimation of End Effector Position in Three-Dimension Robotic Workspace," International Journal of Innovations in Science & Technology, 50sea, vol. 6(5), pages 83-92, May.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:5:p:83-92
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