mahrous, A., Ali, A., Mohamed, S., Othman, M. (2024). Kinematic Analysis and Simulation of an Industrial Rail-Mounted Robot Manipulator Using Ruckig for Enhanced Path Planning. JES. Journal of Engineering Sciences, 52(4), 105-119. doi: 10.21608/jesaun.2024.268475.1312
Abrar mahrous; Ahmed Saad Ali; Shuaiby Mohamed; Mahmoud Othman. "Kinematic Analysis and Simulation of an Industrial Rail-Mounted Robot Manipulator Using Ruckig for Enhanced Path Planning". JES. Journal of Engineering Sciences, 52, 4, 2024, 105-119. doi: 10.21608/jesaun.2024.268475.1312
mahrous, A., Ali, A., Mohamed, S., Othman, M. (2024). 'Kinematic Analysis and Simulation of an Industrial Rail-Mounted Robot Manipulator Using Ruckig for Enhanced Path Planning', JES. Journal of Engineering Sciences, 52(4), pp. 105-119. doi: 10.21608/jesaun.2024.268475.1312
mahrous, A., Ali, A., Mohamed, S., Othman, M. Kinematic Analysis and Simulation of an Industrial Rail-Mounted Robot Manipulator Using Ruckig for Enhanced Path Planning. JES. Journal of Engineering Sciences, 2024; 52(4): 105-119. doi: 10.21608/jesaun.2024.268475.1312
Kinematic Analysis and Simulation of an Industrial Rail-Mounted Robot Manipulator Using Ruckig for Enhanced Path Planning
1Mechatronics Department, Faculty of Engineering, Assiut, Egypt
2Department of Mechatronics Engineering, Faculty of Engineering, Assiut University
Abstract
Robotic manipulators are being widely used in industrial operations and healthcare due to their versatile functionalities. However, the confined workspace of fixed robotic arms limits their applicability in scenarios requiring a broader range of configurations. To overcome this limitation, this research provides a case study on a robotic system composed of two primary subsystems an articulated robotic arm and a linear rail. A simple path planning task was carried out using CoppeliaSim simulation software to study the effect of Ruckig, an advanced online trajectory generation algorithm, alongside the RRT-Connect path planning algorithm. this study demonstrates the capacity of Ruckig to improve the efficiency of path planning regarding the processing time and path length. The results showed that Ruckig helped reducing the process time by 90% with an exceptional improvement to the motion profiles of the system. Regarding the path length, it seems that it was able to decrease the length in certain cases, but not all. The reduction reached a maximum of 50% compared to the original length.
[1] B. Siciliano and O. Khatib, Eds., Springer Handbook of Robotics. in Springer Handbooks. Springer, 2016. Doi: 10.1007/978-3-319-32552-1.
[2] J. J. Craig, Introduction to Robotics. Addison-Wesley Longman, 2005.
[3] K. M. Wurm, C. Stachniss, and W. Burgard, “Coordinated multi-robot exploration using a segmentation of the environment,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 1160–1165. Doi: 10.1109/IROS.2008.4650734.
[4] S. A. Kumar, R. Chand, R. P. Chand, and B. Sharma, “Linear manipulator: Motion control of an n-link robotic arm mounted on a mobile slider,” Heliyon, vol. 9, no. 1, p. e12867, Jan. 2023, Doi: 10.1016/j.heliyon. 2023.e12867.
[5] A. Reiter, Time-Optimal Trajectory Planning for Redundant Robots. Springer Fachmedien Wiesbaden, 2016. Doi: 10.1007/978-3-658-12701-5.
[6] T. Ho, C.-G. Kang, and S. Lee, “Efficient closed-form solution of inverse kinematics for a specific six-DOF arm,” Int J Control Autom Syst, vol. 10, pp. 567–573, Jun. 2012, Doi: 10.1007/s12555-012-0313-9.
[7] H. A. PARK, M. A. ALI, and C. S. G. LEE, “CLOSED-FORM INVERSE KINEMATIC POSITION SOLUTION FOR HUMANOID ROBOTS,” International Journal of Humanoid Robotics, vol. 09, p. 1250022, Sep. 2012, Doi: 10.1142/s0219843612500223.
[8] I. Zaplana, H. Hadfield, and J. Lasenby, “Closed-form solutions for the inverse kinematics of serial robots using conformal geometric algebra,” Mech Mach Theory, vol. 173, p. 104835, 2022.
[9] P. Srisuk, A. Sento, and Y. Kitjaidure, “Inverse kinematics solution using neural networks from forward kinematics equations,” 2017 9th International Conference on Knowledge and Smart Technology (KST), vol. Not available, p. Not available, Feb. 2017, Doi: 10.1109/kst.2017.7886084.
[10] A. Malik, Y. Lischuk, T. Henderson, and R. Prazenica, “A Deep Reinforcement-Learning Approach for Inverse Kinematics Solution of a High Degree of Freedom Robotic Manipulator,” Robotics, vol. 11, no. 2, p. 44, 2022.
[11] N. Wagaa, H. Kallel, and N. Mellouli, “Analytical and deep learning approaches for solving the inverse kinematic problem of a high degrees of freedom robotic arm,” Eng Appl Artif Intell, vol. 123, p. 106301, Aug. 2023, Doi: 10.1016/j.engappai.2023.106301.
[12] H.-H. Huang, C.-K. Cheng, Y.-H. Chen, and H.-Y. Tsai, “The Robotic Arm Velocity Planning Based on Reinforcement Learning,” International Journal of Precision Engineering and Manufacturing, vol. 24, no. 9, pp. 1707–1721, Aug. 2023, Doi: 10.1007/s12541-023-00880-x.
[13] D. Guerin, S. Caro, S. Garnier, and A. Girin, “Optimal measurement pose selection for joint stiffness identification of an industrial robot mounted on a rail,” in 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Jul. 2014. Doi: 10.1109/aim.2014.6878332.
[14] A. Reiter, A. Muller, and H. Gattringer, “On Higher Order Inverse Kinematics Methods in Time-Optimal Trajectory Planning for Kinematically Redundant Manipulators,” IEEE Trans Industr Inform, vol. 14, no. 4, pp. 1681–1690, Apr. 2018, Doi: 10.1109/tii.2018.2792002.
[15] Z. Fan et al., “A Cartesian-Based Trajectory Optimization with Jerk Constraints for a Robot,” Entropy, vol. 25, no. 4, p. 610, Apr. 2023, Doi: 10.3390/e25040610.
[16] H. Pham and Q.-C. Pham, “A New Approach to Time-Optimal Path Parameterization Based on Reachability Analysis,” IEEE Transactions on Robotics, vol. 34, no. 3, pp. 645–659, Jun. 2018, Doi: 10.1109/TRO.2018.2819195.
[17] G. Wu and N. Zhang, “Kinematically Constrained Jerk Continuous S-Curve Trajectory Planning in Joint Space for Industrial Robots,” Electronics (Basel), vol. 12, no. 5, p. 1135, Feb. 2023, Doi: 10.3390/electronics12051135.
[18] D. Pieper, “The kinematics of manipulation under computer control,” Stanford University Stanford, CA, USA, 1968.
[19] A. Farley, J. Wang, and J. A. Marshall, “How to pick a mobile robot simulator: A quantitative comparison of CoppeliaSim, Gazebo, MORSE and Webots with a focus on accuracy of motion,” Simul Model Pract Theory, vol. 120, p. 102629, Nov. 2022, Doi: 10.1016/j.simpat.2022.102629.
[20] R. Paul and B. Shimano, “Kinematic control equations for simple manipulators,” in 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, IEEE, Jan. 1978. Doi: 10.1109/cdc.1978.268148.
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