Advertisement

Soft Computing

, Volume 23, Issue 10, pp 3269–3277 | Cite as

A novel artificial bee colony algorithm for inverse kinematics calculation of 7-DOF serial manipulators

  • Li ZhangEmail author
  • Nanfeng Xiao
Methodologies and Application
  • 164 Downloads

Abstract

In order to overcome the complexity in solving the inverse kinematics calculation of 7-DOF serial manipulator, a new approach CPABC based on artificial bee colony (ABC) algorithm is proposed. CPABC uses the chaotic mapping to optimize the population distribution of the initial food sources to get rid of the local optimization. The whole group of food sources in CPABC is divided into several subgroups which evolve independently and communicate with each other at a certain frequency to improve the convergence rate. To balance the global and local exploitation, two control parameters are introduced to adjust the search step and the change frequency of the optimization parameter when searching the new food source. CPABC is applied to the inverse kinematics calculation of 7-DOF serial manipulator. the simulation results show that CPABC has stronger global searching ability and more fast convergence rate than that of other ABC algorithms.

Keywords

Artificial bee colony algorithm Chaotic map Parallelized Inverse kinematics 7-DOF serial manipulator 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant [No. 61573145], the Public Research and Capacity Building of Guangdong Province under Grant [No. 2014B010104001] and the Basic and Applied Basic Research of Guangdong Province under Grant [No. 2015A03030 8018], and the authors greatly thank these grants.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRefGoogle Scholar
  2. Al-Mashhadany YI (2010) Inverse kinematics problem (ikp) of 6-dof manipulator by locally recurrent neural networks (lrnns). In: 2010 International conference on management and service science (MASS), IEEE, pp 1–5Google Scholar
  3. Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37(8):5682–5687CrossRefGoogle Scholar
  4. Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, OxfordzbMATHGoogle Scholar
  5. Çavdar T, Mohammad M, Milani RA (2013) A new heuristic approach for inverse kinematics of robot arms. Adv Sci Lett 19(1):329–333CrossRefGoogle Scholar
  6. Collins TJ, Shen WM (2016) High-dimensional inverse kinematics and self-reconfiguration kinematic controlGoogle Scholar
  7. Craig JJ (ed) (2005) Introduction to robotics: mechanics and control. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
  8. Goldberg DE (1989) Genetic algorithm in search, optimization, and machine learning xiii(7):2104C2116Google Scholar
  9. Guez A, Ahmad Z (1988) Solution to the inverse kinematics problem in robotics by neural networks. In: IEEE international conference on neural networks, vol 2, pp 617–624Google Scholar
  10. Hartenberg RS, Denavit J (1955) A kinematic notation for lower pair mechanisms based on matrices. J Appl Mech 77(2):215–221MathSciNetzbMATHGoogle Scholar
  11. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech. rep., Technical report-tr06, Erciyes university, engineering faculty, computer engineering departmentGoogle Scholar
  12. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetzbMATHGoogle Scholar
  13. Karlik B, Aydin S (2000) An improved approach to the solution of inverse kinematics problems for robot manipulators. Eng Appl Artif Intell 13(2):159–164CrossRefGoogle Scholar
  14. Karlra P, Prakash NR (2003) A neuro-genetic algorithm approach for solving the inverse kinematics of robotic manipulators. In: IEEE International conference on systems, man and cybernetics, vol 2, pp 1979–1984Google Scholar
  15. Kennedy J, Eberhart R (2002) Particle swarm optimization. In: IEEE Proceedings international conference on neural networks, 1995, vol 4, pp 1942–1948Google Scholar
  16. KöKer R (2013) A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Inf Sci 222:528–543MathSciNetCrossRefzbMATHGoogle Scholar
  17. Luo R, Pan TS, Tsai PW, Pan JS (2010) Parallelized artificial bee colony with ripple-communication strategy. In: 2010 Fourth international conference on genetic and evolutionary computing (ICGEC), pp 350–353Google Scholar
  18. Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf Sci 181(12):2455–2468MathSciNetCrossRefGoogle Scholar
  19. Peña C, Guzmán M, Cárdenas P (2016) Inverse kinematics of a 6 DOF industrial robot manipulator based on bio-inspired multi-objective optimization techniques. In: IEEE Colombian conference on robotics and automation (CCRA), IEEE, pp 1–6Google Scholar
  20. Rokbani N, Alimi AM (2013) Inverse kinematics using particle swarm optimization, a statistical analysis. Proc Eng 64:1602–1611CrossRefGoogle Scholar
  21. Rokbani N, Casals A, Alimi AM (2015) IK-FA, a new heuristic inverse kinematics solver using firefly algorithm. In: Computational intelligence applications in modeling and control, Springer, pp 369–395Google Scholar
  22. Sabat SL, Udgata SK, Abraham A (2010) Artificial bee colony algorithm for small signal model parameter extraction of mesfet. Eng Appl Artif Intell 23(5):689–694CrossRefGoogle Scholar
  23. Sharma TK, Pant M (2011) Halton based initial distribution in artificial bee colony algorithm and its application in software effort estimation. In: 2011 Sixth international conference on bio-inspired computing: theories and applications (BIC-TA), pp 80–84Google Scholar
  24. Sugiarto I, Conradt J (2017) A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation. Robot Auton Syst 91:234–246CrossRefGoogle Scholar
  25. Ziwu R, Zhenhua W, Lining S (2012) A global harmony search algorithm and its application to inverse kinematics problem for humanoid arm. Control Theory Appl 29(7):867–876Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China

Personalised recommendations