Accurate Estimation of Robotic Arm Movements for Effective Motion Control: Utilizing Multiple Sensors and Data Fusion


  • Dler Salih Hasan Department of Computer Science, college of Science, Salahaddin University-Erbil, Kurdistan Region, Iraq



Kinematic Parameters, Sensors Data Fusion, Extended Kalman Filter (EKF), Angular Acceleration, pose Estimation, Robotic Arm


  The robotic manipulators are highly complex coupling dynamic systems, which require a mathematical model for planning and controlling the robotic motions. It is imperative to calculate the kinematic parameters such as rotational matrix, joint angles, angular velocity, and angular acceleration, which determines the control performance of the models. For this purpose, a multiple-sensor-based Mathematical approach that utilizes inertial measurement unit (IMU) and triple-axis accelerometer is presented in this paper. A combination of one IMU and three triple-axis accelerometers is affixed to each of the two rigid bodies for real-time determination of parameters and the robotic arm orientation. Additionally, the model incorporates an Extended Kalman filter (EKF) fusion technique to combine data from various sensors, mitigate measurement noise, and adapt in real-time to changing environments. To implement this approach, a MATLAB code is developed to read, preprocess sensors data, and simulation of the proposed model.  All the results are presented graphically and indicate that the motion parameters and pose measurements are calculated accurately and effectively.


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How to Cite

Salih Hasan, D. (2024). Accurate Estimation of Robotic Arm Movements for Effective Motion Control: Utilizing Multiple Sensors and Data Fusion. Zanco Journal of Pure and Applied Sciences, 36(2), 1–11.



Engineering and Computer Sciences