Skip to main content
SpringerLink
Log in

An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Inverse kinematic solution is a critical procedure for the control of manipulators; however, only a small number of manipulators that meet certain conditions have closed-form solutions. In this paper, the conventional solution was replaced by the optimization process of a proposed fitness function. The fitness function aims to obtain a set of accurate joint angles for the desired position and orientation of the end effector. The function is based on forward kinematics; hence, this approach can avoid singularities and be suitable for solving many manipulators. For solving this optimization problem, particle swarm optimization algorithm was chosen as the basis due to its easy principle and excellent performance. To enhance the performance of the algorithm in searching global optimum, an adaptive inertia weight strategy applied to adjust the velocity of particles was proposed. Besides, for particles, to avoid trapping in local optimum at the boundary, a special boundary treatment was presented. In this study, two serial robotic manipulators were used to test the performance of adaptive particle swarm optimization (APSO). Meanwhile, several powerful PSO variants improved from disparate methods were selected to compare with APSO. The experimental results demonstrate that the proposed fitness function with APSO can solve the inverse kinematic problem of multi-DOF manipulators efficiently and accurately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

All data and material are real. These can be obtained by the correspondence author.

Code availability

All codes of the used algorithms can be provided by the correspondence author.

References

  • Adly MA, and Abd-El-Hafiz SK (2016). Inverse kinematics using single- and multi-objective particle swarm optimization. In: 2016 28th International Conference on Microelectronics (ICM)

  • Alkayyali M, and Tutunji TA (2019). PSO-based algorithm for inverse kinematics solution of robotic arm manipulators. In: 2019 20th International Conference on Research and Education in Mechatronics (REM)

  • Almusawi AR, Dulger LC, Kapucu S (2016) A new artificial neural network approach in solving inverse kinematics of robotic arm (Denso VP6242). Comput Intell Neurosci 2016:5720163

    Article  Google Scholar 

  • Aristidou A, Lasenby J (2011) FABRIK: a fast, iterative solver for the inverse kinematics problem. Graph Models 73(2011):243–260

    Article  Google Scholar 

  • Ayyildiz M, Cetinkaya K (2016) Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator. Neural Comput Appl 27(4):825–836

    Article  Google Scholar 

  • Cai, Z. F., D. P. Huang and Y. Q. Liu (2016). "Inverse Kinematics of Multi-joint Robot Based on Particle Swarm Optimization Algorithm." Journal of Automation and Control Engineering(Vol. 4, No. 4).

  • Çavdar T, Milani RA (2013) A new heuristic approach for inverse kinematics of robot arms. Adv Sci Lett 19(1):329–333

    Article  Google Scholar 

  • Chen YG, Li LX, Peng HP, Xiao JH, Yang YX, Shi YH (2017) Particle swarm optimizer with two differential mutation. Appl Soft Comput 61:314–330

    Article  Google Scholar 

  • Chen YG, Li LX, Xiao JH, Yang YX, Liang J, Li T (2018) Particle swarm optimizer with crossover operation. Eng Appl Artif Intell 70:159–169

    Article  Google Scholar 

  • Collinsm, T. J. and W. M. Shen (2017). Particle swarm optimization for high-DOF inverse kinematics. In: International Conference on Control

  • Corke P (2011) Robotics, vision and control. Springer, Berlin Heidelberg

    Book  Google Scholar 

  • Dereli S, Koker R (2018) IW-PSO approach to the inverse kinematics problem solution of a 7-Dof serial robot manipulator. Int J Nat Eng Sci 36(1):75–85

    Google Scholar 

  • Dereli S, Köker R (2019) A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm. Artif Intell Rev 53(2):949–964

    Article  Google Scholar 

  • Dereli S, Koker R (2021) Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem. Complex Intell Syst 7(3):1515–1526

    Article  Google Scholar 

  • Du Y, and Y Wu (2011). Application of IPSO algorithm to inverse kinematics solution of reconfigurable modular robots. In: 2011 International Conference on Mechatronic Science, Electric Engineering and Computer. Jilin, China

  • Durmuş B, Temurtaş H and Gün A (2011). An inverse kinematics solution using particle swarm optimization. In: 6th International Advanced Technologies Symposium. Elazığ, Turkey

  • Eberhart R, and Kennedy J (2002). A new optimizer using particle swarm theory. In: Mhs95 Sixth International Symposium on Micro Machine & Human Science

  • Falconi R, Grandi R, C and Melchiorri, (2014) Inverse kinematics of serial manipulators in cluttered environments using a new paradigm of particle swarm optimizsation. IFAC Proc 47:8475–8480

    Article  Google Scholar 

  • Featherstone R (1983) Position and velocity transformations between robot end-effector coordinates and joint angles. Int J Robot Res 2(2):35–45

    Article  Google Scholar 

  • Huang H-C, Chen C-P and Wang P-R (2012). Particle swarm optimization for solving the inverse kinematics of 7-DOF robotic manipulators. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics. COEX, Seoul, Korea

  • Jin M, Liu Q, Wang B, Liu H (2020) An efficient and accurate inverse kinematics for 7-DOF redundant manipulators based on a hybrid of analytical and numerical method. IEEE Access 8:16316–16330

    Article  Google Scholar 

  • Junior JDLS, RCDO Jesus, L Molina, EAN Carvalho and EO Freire (2018). FRPSO: inverse kinematics using fully resampled particle swarm optimization. In: Latin American Robotic SymposiumBrazilian Symposium on RoboticsWorkshop on Robotics in Education

  • Kker R, Akar T, Sari Y (2014) A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators. Eng Comput 30(4):641–649

    Article  Google Scholar 

  • Koeker R (2005) Reliability-based approach to the inverse kinematics solution of robots using Elman’s networks. Eng Appl Artif Intell 18(6):685–693

    Article  Google Scholar 

  • Lee CSG, Ziegler M (1984) Geometric approach in solving inverse kinematics of PUMA robots. IEEE Trans Aerosp Electr Sys 6:695–706

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Manocha D, Canny JF (1994) Efficient inverse kinematics for general 6R manipulators. IEEE Trans Robot Autom 10(5):648–657

    Article  Google Scholar 

  • Manocha D, and Zhu Y (1994). A fast algorithm and system for the inverse kinematics of general serial manipulators. In: IEEE International Conference on Robotics & Automation

  • Mavroidis C, Roth B (1994) Structural parameters which reduce the number of manipulator configurations. J Mech Des 116(1):3–10

    Article  Google Scholar 

  • Netjinda N, Achalakul T, Sirinaovakul B (2015) Particle swarm optimization inspired by starling flock behavior. Appl Soft Comput 35:411–422

    Article  Google Scholar 

  • Nizar R, Casals A, Alimi AM (2015) IK-FA, a new heuristic inverse kinematics solver using firefly algorithm. Comput Intell Appl Model Control 575:553–565

    Google Scholar 

  • Ozgoren MK (2013) Optimal inverse kinematic solutions for redundant manipulators by using analytical methods to minimize position and velocity measures. J Mech Robot Trans Asme 5(3):031009

    Article  Google Scholar 

  • Pant M, Thangaraj R, and Abraham A (2008) A new quantum behaved particle swarm optimization. In: The 10th annual conference on genetic and evolutionary computation, ACM

  • Qin Z and Liang YG (2016) A study on the particle swarm optimization with adaptive weight constrained layout optimization. In: 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (Ihmsc), 1: 283–287

  • Ram RV, Pathak PM, Junco SJ (2018) Inverse kinematics of mobile manipulator using bidirectional particle swarm optimization by manipulator decoupling. Mech Mach Theory 131:385–405

    Article  Google Scholar 

  • Rokbani N, Alimi AM (2013) Inverse kinematics using particle swarm optimization, a statistical analysis. Procedia Eng 64:1602–1611

    Article  Google Scholar 

  • Spong M, Hutchinson S, Vidyasagar M (2020) Robot modeling and control. Wiley, London

    Google Scholar 

  • Starke S, Hendrich N, Magg S and Zhang J (2017). An efficient hybridization of genetic algorithms and particle swarm optimization for inverse kinematics. In: IEEE International Conference on Robotics and Biomimetics

  • Sui Z, Jiang L, Tian Y-T and Jiang W (2015). Genetic algorithm for solving the inverse kinematics problem for general 6R robots. In: Proceedings of the 2015 Chinese Intelligent Automation Conference: 151–161.

  • Ting R, Jing-Wei Z, Xin-Sheng J (2010) Solving PUMA robot inverse kinematics based on simulated annealing particle swarm optimization. Comput Eng Appl 47(22):160–163

    Google Scholar 

  • Uzcátegui CG, Rojas DB (2013) A memetic differential evolution algorithm for the inverse kinematics problem of robot manipulators. Int J Mech Autom 3(2):118–131

    Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Yang Y, Peng G, Wang Y and Zhang H (2007). A new solution for inverse kinematics of 7-DOF manipulator based on genetic algorithm

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Petroleum University, Chengdu, China

    Heng Deng & Chong Xie

Authors
  1. Heng Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HD and CX. The first draft of the manuscript was written by HD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chong Xie.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, H., Xie, C. An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators. Soft Comput 25, 13695–13708 (2021). https://doi.org/10.1007/s00500-021-06007-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06007-6

Keywords

Search

Navigation