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

Dler Salih Hasan

doi:10.21271/ZJPAS.36.2.1

Authors

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

DOI:

https://doi.org/10.21271/ZJPAS.36.2.1

Keywords:

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

Abstract

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.

References

Craig, J.J. (2005) Introduction to Robotics: Mechanics and Control. Addison-Wesley Longman.

Cvitanic, T., Melkote, S.N. and Balakirsky, S. (2022) “Improved state estimation of a robot end-effector using laser tracker and inertial sensor fusion,” Cirp Journal of Manufacturing Science and Technology, 38, pp. 51–61. Available at: https://doi.org/10.1016/j.cirpj.2022.03.011.

Dler Salih Hasan, Carl Crane III, Ibrahim Isamel Hamarash, (2019) “Using Inertia Sensors for Orientation Estimation of Robot Manipulators.” ZANCO Journal of Pure and Applied Sciences 31(s3),https://doi.org/10.21271/zjpas.31.s3.44

Du, G. and Zhang, P. (2014) “Online Serial Manipulator Calibration Based on Multisensory Process Via Extended Kalman and Particle Filters,” IEEE Transactions on Industrial Electronics, 61(12), pp. 6852–6859. Available at: https://doi.org/10.1109/tie.2014.2314051.

Kang, Y.C., Le, Q.N. and Jeon, J.W. (2009) “An FPGA-Based Multiple-Axis Motion Control Chip,” IEEE Transactions on Industrial Electronics, 56(3), pp. 856–870. Available at: https://doi.org/10.1109/tie.2008.2004671.

Kim, H., Tanaka, Y., Kawamura, A., Kawamura, S., and Nishioka, Y (2015) Improvement of position accuracy for inflatable robotic arm using visual feedback control method. Available at: https://doi.org/10.1109/aim.2015.7222630.

Kinjal, V., Dholariya, T. R., & Patel, C. (2015)., “Multidimentional Motion Control of Robotic Arm.” International Journal of Innovative Research in Computer and Communication Engineering, 3( 2).

Laidig, D. et al. (2021) “Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors,” Frontiers in Digital Health, 3. Available at: https://doi.org/10.3389/fdgth.2021.736418.

Lapusan, C., Hancu, O. and Rad, C. (2022) “Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network,” Sensors, 22(1), p. 373. Available at: https://doi.org/10.3390/s22010373.

Liu, B., Zhang, F. and Qu, X. (2015) “A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion,” Sensors, 15(4), pp. 7933–7952. Available at: https://doi.org/10.3390/s150407933.

McLean, L. (2018) High-Order Robotic Joint Sensing with Multiple Accelerometer and Gyroscope Systems. Available at: https://digital.library.adelaide.edu.au/dspace/bitstream/2440/120357/1/McLean2018_MPhil.pdf.

Murray, R.M., Sastry, S.S. and Zexiang, L. (2017) A Mathematical Introduction to Robotic Manipulation, CRC Press eBooks. Available at: https://doi.org/10.1201/9781315136370.

Neurauter, R. and Gerstmayr, J. (2023) “A novel motion-reconstruction method for inertial sensors with constraints,” Multibody System Dynamics, 57(2), pp. 181–209. Available at: https://doi.org/10.1007/s11044-022-09863-8.

Rahman, Md.M. Mahmudur, Kok Beng Gan, Noor Azah Abd Aziz, Audrey Huong, and Huay Woon You (2023) “Upper Limb Joint Angle Estimation Using Wearable IMUs and Personalized Calibration Algorithm,” Mathematics, 11(4), p. 970. Available at: https://doi.org/10.3390/math11040970.

Spong, M.W., Hutchinson, S. and Vidyasagar, M. (2006) Robot Modeling and Control. Available at: https://www.control.isy.liu.se/en/student/graduate/robot/files/courseInfo.pdf.

Thomas, R.O. and Rajasekaran, K. (2014) “Remote Monitoring and Control of Robotic Arm with Visual Feedback using Raspberry Pi,” International Journal of Computer Applications, 92(9), pp. 29–32. Available at: https://doi.org/10.5120/16040-5075.

Tusset, A.M. et al. (2023) “Positioning Control of Robotic Manipulators Subject to Excitation from Non-Ideal Sources,” Robotics, 12(2), p. 51. Available at: https://doi.org/10.3390/robotics12020051.

Van Heerden, K. (2017) “Real-Time Variable Center of Mass Height Trajectory Planning for Humanoids Robots,” IEEE Robotics and Automation Letters, 2(1), pp. 135–142. Available at: https://doi.org/10.1109/lra.2016.2579741.

Xiaoping, Y., Bachmann, E.R. and McGhee, R.B. (2008) “A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements,” IEEE Transactions on Instrumentation and Measurement, 57(3), pp. 638–650. Available at: https://doi.org/10.1109/tim.2007.911646.