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A novel inverse kinematics for solving repetitive motion planning of 7-DOF SRS manipulator

Published online by Cambridge University Press:  06 October 2022

Jingdong Zhao ,
Zichun Xu Open the ORCID record for Zichun Xu [Opens in a new window] ,
Liangliang Zhao ,
Yuntao Li ,
Liyan Ma  and
Hong Liu
Jingdong Zhao
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
Zichun Xu
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
Liangliang Zhao*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
Yuntao Li
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
Liyan Ma
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
Hong Liu
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
*
*Corresponding author. E-mail: zhaoliangliang0619@126.com
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Abstract

The repetitive motion planning movements of the redundant manipulator will cause oscillations and unintended swings of joints, which increase the risk of collisions between the manipulator and its surroundings. Motivated by this phenomenon, this paper presents an inverse kinematics algorithm for the spherical-revolute-spherical manipulator to solve the paradox raised by joint-drift and control the pose with no swing of the elbow. This algorithm takes the joint Cartesian positions set as the intermediary and divides the inverse solution process into two mapping processes within joint limits. Simulations are executed to evaluate this algorithm, and the results show this algorithm is applicable to repetitive motion planning and is capable of producing superior configurations based on its real-time ability and stable solve rate. Experiments using the 7-degree-of-freedom spherical-revolute-spherical manipulator demonstrate the effectiveness of this algorithm to remedy the joint-drift and elbow swing compared to Kinematics and Dynamics Library and TRAC-IK.

Keywords

inverse kinematicsredundant manipulatorsjoint angle driftrepetitive motion planning
Type
Research Article
Information
Robotica , Volume 41 , Issue 1 , January 2023 , pp. 392 - 409
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Zhao, L., Zhao, J. and Liu, H., “Solving the inverse kinematics problem of multiple redundant manipulators with collision avoidance in dynamic environments,” J. Intell. Robot. Syst. 101(2), 30(2021).CrossRefGoogle Scholar
Zhao, L., Jiang, Z., Sun, Y., Zhao, J. and Liu, H., “Collision-free kinematics for hyper-redundant manipulators in dynamic scenes using optimal velocity obstacles,” Int. J. Adv. Robot. Syst. 18(1), 1–17 (2021).CrossRefGoogle Scholar
Malhan, R. K., Kabir, A. M., Shah, B. and Gupta, S. K., “Identifying Feasible Workpiece Placement with Respect to Redundant Manipulator for Complex Manufacturing Tasks,” In: IEEE International Conference on Robotics and Automation (ICRA), IEEE (2019) pp. 5585–5591.Google Scholar
Mu, Z., Liu, T., Xu, W., Lou, Y. and Liang, B., “A hybrid obstacle-avoidance method of spatial hyper-redundant manipulators for servicing in confined space,” Robotica 37(6), 998–1019 (2019).CrossRefGoogle Scholar
Klein, C. A. and Huang, C.-H., “Review of pseudoinverse control for use with kinematically redundant manipulators,” IEEE Trans. Syst. Man Cybern. 13(2), 245–250 (1983).CrossRefGoogle Scholar
Li, Z., Xu, F., Guo, D., Wang, P. and Yuan, B., “New P-type RMPC scheme for redundant robot manipulators in noisy environment,” Robotica 38(5), 775–786 (2020).CrossRefGoogle Scholar
Li, Z., Li, C., Li, S. and Cao, X., “A fault-tolerant method for motion planning of industrial redundant manipulator,” IEEE Trans. Ind. Inform. 16(12), 7469–7478 (2019).CrossRefGoogle Scholar
Zhang, Z., Fu, T., Yan, Z., Jin, L., Xiao, L., Sun, Y., Yu, Z. and Li, Y., “A varying-parameter convergent-differential neural network for solving joint-angular-drift problems of redundant robot manipulators,” IEEE/ASME Trans. Mech. 23(2), 679–689 (2018).CrossRefGoogle Scholar
Chiacchio, P. and Siciliano, B., “A closed-loop jacobian transpose scheme for solving the inverse kinematics of nonredundant and redundant wrists,” J. Robot. Syst. 6(5), 601–630 (1989).CrossRefGoogle Scholar
Wang, J., Li, Y. and Zhao, X., “Inverse kinematics and control of a 7-DOF redundant manipulator based on the closed-loop algorithm,” Int. J. Adv. Robot. Syst. 7(4), 37–47 (2010).CrossRefGoogle Scholar
Wampler, C. W., “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16(1), 93–101 (1986).CrossRefGoogle Scholar
Nakamura, Y. and Hanafusa, H., “Inverse kinematic solutions with singularity robustness for robot manipulator control,” ASME J. Dyn. Syst. Meas. Cont. 108(3), 163–171 (1986).CrossRefGoogle Scholar
Beeson, P. and Ames, B., “TRAC-IK: An Open-Source Library for Improved Solving of Generic Inverse Kinematics,” In: IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), IEEE (2015) pp. 928–935.Google Scholar
Yoshikawa, T., “Manipulability of robotic mechanisms,” Int. J. Robot. Res. 4(2), 3–9 (1985).CrossRefGoogle Scholar
Abbasi, V., Azria, B., Tabarah, E., Menon, V., Phillips, E. and Bedirian, M., “Improved 7-DOF Control of ISS Robotic Manipulators,” In: Space OPS, 2004 Conference (2004) pp. 407.Google Scholar
Lu, H., Jin, L., Zhang, J., Sun, Z., Li, S. and Zhang, Z., “New joint-drift-free scheme aided with projected ZNN for motion generation of redundant robot manipulators perturbed by disturbances,” IEEE Trans. Syst. Man Cybern. Syst. 51(9), 5639–5651 (2019).CrossRefGoogle Scholar
Jin, L., Xie, Z., Liu, M., Chen, K., Li, C. and Yang, C., “Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated,” IEEE/ASME Trans. Mech. 26(1), 90–101 (2020).Google Scholar
Guo, D., Li, Z., Khan, A. H., Feng, Q. and Cai, J., “Repetitive motion planning of robotic manipulators with guaranteed precision,” IEEE Trans. Ind. Inform. 17(1), 356–366 (2020).CrossRefGoogle Scholar
Wang, L.-C. and Chen, C.-C., “A combined optimization method for solving the inverse kinematics problems of mechanical manipulators,” IEEE Trans. Robot. Autom. 7(4), 489–499 (1991).CrossRefGoogle Scholar
Aristidou, A., Lasenby, J., Chrysanthou, Y. and Shamir, A., “Inverse kinematics techniques in computer graphics: A survey,” Comput. Graph. Forum 37(6), 35–58 (2018).CrossRefGoogle Scholar
Xu, W., Yan, L., Mu, Z. and Wang, Z., “Dual arm-angle parameterisation and its applications for analytical inverse kinematics of redundant manipulators,” Robotica 34(12), 2669–2688 (2016).CrossRefGoogle Scholar
Ma, B., Xie, Z., Jiang, Z. and Liu, H., “Precise semi-analytical inverse kinematic solution for 7-DOF offset manipulator with arm angle optimization,” Front. Mech. Eng. 16(3), 435–450 (2021).CrossRefGoogle Scholar
Oh, J., Bae, H. and Oh, J.-H., “Analytic Inverse Kinematics Considering the Joint Constraints and Self-Collision for Redundant 7DOF Manipulator,” In: First IEEE International Conference on Robotic Computing (IRC), IEEE (2017) pp. 123–128.Google Scholar
Sinha, A. and Chakraborty, N., “Geometric Search-based Inverse Kinematics of 7-DoF Redundant Manipulator with Multiple Joint Offsets,” In: International Conference on Robotics and Automation (ICRA), IEEE (2019) pp. 5592–5598.Google Scholar
Chirikjian, G. S. and Burdick, J. W., “A modal approach to hyper-redundant manipulator kinematics,” IEEE Trans. Robot. Autom. 10(3), 343–354 (1994).CrossRefGoogle Scholar
Oyama, E., Chong, N. Y., Agah, A. and Maeda, T., “Inverse Kinematics Learning by Modular Architecture Neural Networks with Performance Prediction Networks,” In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), IEEE (2001) pp. 1006–1012.Google Scholar
Marconi, G. M., Camoriano, R., Rosasco, L. and Ciliberto, C., “Structured prediction for CRiSP inverse kinematics learning with misspecified robot models,” IEEE Robot. Autom. Lett. 6(3), 5650–5657 (2021).CrossRefGoogle Scholar
Xie, Z., Jin, L., Luo, X., Hu, B. and Li, S., “An acceleration-level data-driven repetitive motion planning scheme for kinematic control of robots with unknown structure,” IEEE Trans. Syst. Man Cybern. Syst. 52(9), 5679–5691 (2021).CrossRefGoogle Scholar
Hassan, A. A., El-Habrouk, M. and Deghedie, S., “Inverse kinematics of redundant manipulators formulated as quadratic programming optimization problem solved using recurrent neural networks: A review,” Robotica 38(8), 1495–1512 (2020).CrossRefGoogle Scholar
Xie, Z., Jin, L., Du, X., Xiao, X., Li, H. and Li, S., “On generalized RMP scheme for redundant robot manipulators aided with dynamic neural networks and nonconvex bound constraints,” IEEE Trans. Ind. Inform. 15(9), 5172–5181 (2019).CrossRefGoogle Scholar
Li, Z., Li, C., Li, S., Zhu, S. and Samani, H., “A sparsity-based method for fault-tolerant manipulation of a redundant robot,” Robotica 40(10), 3396–3414 (2022).CrossRefGoogle Scholar
Starke, S., Hendrich, N., Magg, S. and Zhang, J., “An Efficient Hybridization of Genetic Algorithms and Particle Swarm Optimization for Inverse Kinematics,” In: IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE (2016) pp. 1782–1789.Google Scholar
Starke, S., Hendrich, N. and Zhang, J., “Memetic evolution for generic full-body inverse kinematics in robotics and animation,” IEEE Trans. Evol. Comput. 23(3), 406–420 (2019).CrossRefGoogle Scholar

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