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Improved dung beetle optimization algorithm based inverse kinematics solution for robotic arm

Published online by Cambridge University Press:  30 September 2025

Yunpeng Lv Open the ORCID record for Yunpeng Lv [Opens in a new window] ,
Hongbing Li ,
Siqi Zhu ,
Siyun Tan ,
Peng Yang  and
Chunzhe Zhao
Yunpeng Lv
Affiliation:
Chongqing Key Laboratory of Geological Environment Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing, China
Hongbing Li*
Affiliation:
Municipal Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, China Chongqing Engineering Research Center for Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, China
Siqi Zhu
Affiliation:
Municipal Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, China Chongqing Engineering Research Center for Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, China
Siyun Tan
Affiliation:
Municipal Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, China Chongqing Engineering Research Center for Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, China
Peng Yang
Affiliation:
Chongqing Key Laboratory of Geological Environment Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing, China
Chunzhe Zhao
Affiliation:
Municipal Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, China Chongqing Engineering Research Center for Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, China
*
Corresponding author: Hongbing Li; Email: cqlhb@qq.com
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Abstract

Aiming at the issues of more difficult to solve and lower precision of six-axis robotic arm in inverse kinematics (IK) solution, a multi-strategy improved dung beetle optimization algorithm (ECDBO) is proposed. It improves performance in four aspects: population initiation, global search capability, search direction perturbation and jumping out of local optima. Sobol sequence strategy was introduced to initialize the dung beetle population, resulting in a more even distribution of individual dung beetles and increasing the diversity of initial population. Boundary optimization strategy is adopted to balance the requirements on search capability at different times. This approach enhances global search capability at the beginning and local search capability at the end of an iteration. Propose hybrid directional perturbation strategy to change the search direction of rolling dung beetles and stealing dung beetles. It allows for more detailed exploration and improves convergence accuracy. The Levy flight strategy is incorporated to perturb current optimal solution, enhancing algorithm’s ability to jump out of the local optimum. In order to verify performance of ECDBO algorithm, CEC2017 function tests and robotic arm IK solving experiments were conducted and compared with other algorithms. ECDBO ranked first on 21 functions in the 30 dimensions tested in CEC2017 and on 27 functions in the 100 dimensions. ECDBO performs well in the IK solving experiments of two robotic arms with better accuracy than other algorithms. The experimental results show that the ECDBO algorithm significantly improves the convergence and accuracy, and also performs excellently on the IK solving problem.

Keywords

dung beetle optimization algorithmdirectional perturbationinverse kinematics solutionRobotic HandsSerial Manipulator Design and Kinematics

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 31
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Luo, Y. Y., Li, D. X. and Fan, C. Z., “An optimization method for solution selection based on the inverse kinematics problem of a mechanical arm,” Appl. Mech. Mater. 740, 211–217 (2015).10.4028/www.scientific.net/AMM.740.211CrossRefGoogle Scholar
Angeles, J., “On the numerical solution of the inverse kinematic problem,” Int. J. Robot. Res. 4(2), 21–37 (1985).10.1177/027836498500400203CrossRefGoogle Scholar
Uzuner, S., Akkuş, N. and Toz, M., “5-DOF serial robot manipulator design, application and inverse kinematic solution through analytical method and simple search technique,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26(2), 392–401 (2020).Google Scholar
Xie, S., Sun, L., Chen, G., Wang, Z. and Wang, Z., “A novel solution to the inverse kinematics problem of general 7R robots,” IEEE Access 10, 67451–67469 (2022).10.1109/ACCESS.2022.3184451CrossRefGoogle Scholar
Bai, Y., Luo, M. and Pang, F., “An algorithm for solving robot inverse kinematics based on FOA optimized BP neural network,” Appl. Sci. 11(15), 7129 (2021).CrossRefGoogle Scholar
Luo, S., Chu, D., Li, Q. and He, Y., “Inverse kinematics solution of 6-DOF manipulator based on multi-objective full-parameter optimization PSO algorithm,” Front. Neurorobotics 16, 791796 (2022).10.3389/fnbot.2022.791796CrossRefGoogle ScholarPubMed
Thomas, M. J., Sanjeev, M. M., Sudheer, A. and J., M. L., “Comparative study of various machine learning algorithms and Denavit–Hartenberg approach for the inverse kinematic solutions in a 3- PP SS parallel manipulator,” Ind. Robot. 47(5), 683–695 (2020).10.1108/IR-11-2019-0233CrossRefGoogle Scholar
Motahari, A., “Solving the inverse kinematics problem of discretely actuated hyper-redundant manipulators using the multi-module searching method,” Robotica 42(3), 911–930 (2024).10.1017/S0263574723001893CrossRefGoogle Scholar
Guo, D., Li, A., Cai, J., Feng, Q. and Shi, Y., “Inverse kinematics of redundant manipulators with guaranteed performance,” Robotica 40(1), 170–190 (2022).10.1017/S026357472100045XCrossRefGoogle Scholar
Dereli, S. and Köker, R., “Simulation based calculation of the inverse kinematics solution of 7-DOF robot manipulator using artificial bee colony algorithm,” SN Appl. Sci. 2(1), 27 (2020).10.1007/s42452-019-1791-7CrossRefGoogle Scholar
Deng, H. and Xie, C., “An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators,” Soft Comput. 25(21), 13695–13708 (2021).10.1007/s00500-021-06007-6CrossRefGoogle Scholar
Zhong, J., Wang, T. and Cheng, L., “Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics,” Complex Intell. Syst. 8(3), 1899–1912 (2022).10.1007/s40747-021-00366-1CrossRefGoogle Scholar
Xiao, Y., Cui, H., Khurma, R. A. and Castillo, P. A., “Artificial lemming algorithm: A novel bionic meta-heuristic technique for solving real-world engineering optimization problems,” Artif. Intell. Rev. 58(3), 84 (2025).10.1007/s10462-024-11023-7CrossRefGoogle Scholar
Xiao, Y., Cui, H., Hussien, A. G. and Hashim, F. A., “MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications,” Adv. Eng. Inform. 61(3), 102464 (2024).10.1016/j.aei.2024.102464CrossRefGoogle Scholar
Zhong, R., Yu, J., Zhang, C. and Munetomo, M., “SRIME: A strengthened RIME with Latin hypercube sampling and embedded distance-based selection for engineering optimization problems,” Neural Comput. Appl. 36(12), 6721–6740 (2024).CrossRefGoogle Scholar
Zhong, R., Fan, Q., Zhang, C. and Yu, J., “Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimization,” Cluster Comput. 27(7), 10141–10168 (2024).10.1007/s10586-024-04508-1CrossRefGoogle Scholar
Xue, J. and Shen, B., “Dung beetle optimizer: A new meta-heuristic algorithm for global optimization,” J. Supercomput. 79(7), 7305–7336 (2023).10.1007/s11227-022-04959-6CrossRefGoogle Scholar
Wolpert, D. H. and Macready, W. G., “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput. 1(1), 67–82 (1997).10.1109/4235.585893CrossRefGoogle Scholar
Zhu, F., Li, G., Tang, H., Li, Y., Lv, X. and Wang, X., “Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems,” Expert Syst. Appl. 236(2), 121219 (2024).10.1016/j.eswa.2023.121219CrossRefGoogle Scholar
Zhang, R. and Zhu, Y., “Predicting the mechanical properties of heat-treated woods using optimization-algorithm-based BPNN,” Forests 14(5), 935 (2023).CrossRefGoogle Scholar
Li, L., Liu, L., Shao, Y., Zhang, X., Chen, Y., Guo, C. and Nian, H., “Enhancing swarm intelligence for obstacle avoidance with multi-strategy and improved dung beetle optimization algorithm in mobile robot navigation,” Electronics 12(21), 4462 (2023).10.3390/electronics12214462CrossRefGoogle Scholar
Zilong, W. and Peng, S., “A multi-strategy dung beetle optimization algorithm for optimizing constrained engineering problems,” IEEE Access 11, 98805–98817 (2023).10.1109/ACCESS.2023.3313930CrossRefGoogle Scholar
Mirjalili, S. and Lewis, A., “The whale optimization algorithm,” Adv. Eng. Softw. 95(5), 51–67 (2016).10.1016/j.advengsoft.2016.01.008CrossRefGoogle Scholar
Arora, S. and Singh, S., “Butterfly optimization algorithm: A novel approach for global optimization,” Soft Comput. 23(3), 715–734 (2019).10.1007/s00500-018-3102-4CrossRefGoogle Scholar
Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” In: Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4, IEEE (1995) pp. 1942–1948.10.1109/ICNN.1995.488968CrossRefGoogle Scholar
Abed-alguni, B. H., “Island-based cuckoo search with highly disruptive polynomial mutation,” Int. J. Artif. Intell. 17(1), 57–82 (2019).Google Scholar
Tanyildizi, E. and Demir, G., “Golden sine algorithm: A novel math-inspired algorithm,” Adv. Electr. Comput. Eng. 17(2), 71–79 (2017).10.4316/AECE.2017.02010CrossRefGoogle Scholar