Automated Inverse Kinematics Configuration Selection for Path Planning of a 6-DOF Robot

Xuan-Vinh Nguyen; Ngoc-Lam Nguyen

doi:10.59796/jcst.V14N1.2024.10

Authors

Xuan-Vinh Nguyen Faculty of Electronics and Telecommunications, University of Science, Ho Chi Minh City, Vietnam & Vietnam National University, Ho Chi Minh City, Vietnam
Ngoc-Lam Nguyen Vietnam Research Institute of Electronics, Informatics and Automation

DOI:

https://doi.org/10.59796/jcst.V14N1.2024.10

Keywords:

6-DOF robot, kinematics, workspace, path planning

Abstract

This article presents an automated technique for selecting suitable inverse configurations for the path planning of robots with six degrees of freedom (6 DOF). Traditionally, robots were limited to a single fixed configuration for movement, but now there's a growing need for robots that can adapt to different situations, especially in unknown environments. In this study, we introduce an innovative approach designed to assist a small industrial robot known as AKB-IRV1. This approach helps the robot determine and automatically select the most suitable configuration to move, which is essential for effective path planning. We simplify a complex problem related to how the robot's joints work through geometric analysis, breaking it down into two stages: calculation and selection of the best joint angles for movement. We also use computer simulations to assess the robot's workspace, considering joint angles as constraints. Our findings reveal that taking joint angles into account significantly reduces the robot's effective workspace. We also present a method for the robot to automatically choose the right configuration when planning its path, especially in uncertain situations. This ability allows the robot to change its configuration as needed, aligning with the goal of minimizing configuration changes. This method has promising applications for intelligent robots operating in unfamiliar environments.

References

Abderrahim, B., Moulay, E. H. E. C., Hassan S., & Hicham A. E., (2023). The Inverse Kinematics Evaluation of 6-DOF Robots in Cooperative Tasks Using Virtual Modeling Design and Artificial Intelligence Tools. The International Journal of Robotics Research, 12(2), 121-130. https://doi.org/10.18178/ijmerr.12.2.121-130

Ahmed, R. J. A., Canan, D. L., & Sadettin, K. (2016). A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242). Computational Intelligence and Neuroscience, 2016, Article 5720163. https://doi.org/10.1155/2016/5720163

AKB Machinery. (n.d.). AKB-IRV1 6-DOF robot. Retrieved from https://akbmachinery.com/san-pham/canh-tay-robot-6-bac-tu-do-akb-irv1/

Chembuly, V. V. M. J. S., & Voruganti, H. K. (2020). An efficient approach for inverse kinematics and redundancy resolution of spatial redundant robots for cluttered environment. SN Applied Sciences, 2, Article 1012. https://doi.org/10.1007/s42452-020-2825-x

Chen, Q., Zhu, S., & Zhang, X. (2015). Improved Inverse Kinematics Algorithm Using Screw Theory for a Six-DOF Robot Manipulator, International Journal of Advanced Robotic Systems, 12(10), Article 60834. https://doi.org/10.5772/60834

Craig, J. J. (2005). Intrduction to Robotics: Mechanics and Control (3rd ed.). New Jersey: Prentice Hall.

Denavit, J. & Hartenberg, R. (1955). A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices. Journal of Applied Mechanics, 22(2), 215–221. https://doi.org/10.1115/1.4011045

Gasparetto, A., Boscariol, P., Lanzutti, A., & Vidoni, R. (2015). Path Planning and Trajectory Planning Algorithms: A General Overview. In: Carbone, G., Gomez-Bravo, F. (eds) Motion and Operation Planning of Robotic Systems. Mechanisms and Machine Science, 29. Cham: Springer, https://doi.org/10.1007/978-3-319-14705-5_1

Gracia, L., Andres, J. & Tornero, J. (2009). Trajectory Tracking with a 6R Serial Industrial Robot with Ordinary and Non-ordinary Singularities. International Journal of Control, Automation, and Systems, 7, 85-96. https://doi.org/10.1007/s12555-009-0111-1

Hartenberg, R. & Denavit, J. (1964). Kinematic Synthesis of Linkages (1st ed.). McGraw Hill.

Hayes, M. J. D., Husty, M. L., & Zsombor-Murray, P. J. (2003). Singular Configurations of Wrist-Partitioned 6R Serial Robots: A Geometric Perspective for Users. Transactions of the Canadian Society for Mechanical Engineering, 26(1), 41-55. https://doi.org/10.1139/tcsme-2002-0003.

Husi, G. (2015). Position Singularities and Ambiguities of the KUKA KR5 Robot. International Journal of Engineering Technologies IJET, 1(1), 44–50. https://doi.org/10.19072/ijet.105700

Iqbal, J., Islam, R. & Khan, H. (2012). Modeling and Analysis of a 6 DOF Robotic Arm Manipulator. Canadian Journal on Electrical and Electronics Engineering, 3(6), 300–306.

Isiah, Z. & Luis, B. (2017). A novel closed-form solution for the inverse kinematics of redundant manipulators through workspace analysis. Mechanism and Machine Theory, 121, 829-843. https://doi.org/10.1016/j.mechmachtheory.2017.12.005

Kshitish, K. D., Bibhuti, B. C., & Sukanta, K. S. (2017). A Inverse Kinematic Solution Of A 6-DOF Industrial Robot Using ANN. Indian Journal of Social Science Research, 15(2), 97–101

Lee, C. S. G., & Ziegler, M. (1984). A Geometric Approach in Solving the Inverse Kinematics of Puma Robots. IEEE Transactions on Aerospace and Electronic Systems, AES-20(6), 695–706. https://doi.org/10.1109/TAES.1984.310452

Perumaal, S. S., & Jawahar N. (2012). Automated Trajectory Planner of Industrial Robot for Pick-and-Place Task. International Journal of Advanced Robotic Systems, 10(2), Article 53940. https://doi.org/10.5772/53940

Piotrowski, N., & Barylski, A. (2014). Modelling a 6-DOF Manipulator using Matlab software. Archives of Mechanical Technology and Automation, 34(3), 45–55

Siméon, T., Laumond J. P., Cortés, J., & Sahbani A. (2004). Manipulation Planning with Probabilistic Roadmaps. The International Journal of Robotics Research, 23(7–8), 729–746. https://doi.org/10.1177/0278364904045471

Spong, M. W., Hutchinson, S., & Vidyasagar, M., (2020). Robot Modelling and Control (2nd ed.), New Jersey: John Wiley & Sons, Inc.

Spong, M. W., Hutchinson, S., &Vidyasagar, M., (2004). Robot Dynamic and Control. (2nd ed.), Academia.edu Publishing.

Tagliani, F. L., Pellegrini, N., & Aggogeri, F. (2022). Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling. Applied Sciences, 12(19), 9417. https://doi.org/10.3390/app12199417.

Wang, X., Zhang, D., & Zhao C., (2017). The inverse kinematics of a 7R 6-degree-of-freedom robot with non-spherical wrist. Advances in Mechanical Engineering, 9(8). https://doi.org/10.1177/1687814017714985

Wang, X., Cao, J., Chen, L., & Hu, H. (2020). Two Optimized General Methods for Inverse Kinematics of 6R, Robots Based on Machine Learning. Mathematical Problems in Engineering, 2020, Article 8174924. https://doi.org/10.1155/2020/8174924.

Yiyang, L., Xi, J., Hongfei, B., Zhining, W. & Liangliang, S. (2021), A General Robot Inverse Kinematics Solution Method Based on Improved PSO Algorithm. IEEE Access, 9, 32341–32350. https://doi.org/10.1109/ACCESS.2021.3059714

Zhang, D. & Hannaford, B. (2019). IKBT: solving symbolic inverse kinematics with behavior tree. Journal of Artificial Intelligence Research, 65(1), 457–486. https://doi.org/10.1613/jair.1.115