Skip to main content
Log in

Inverse kinematics of a mobile robot

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The problem of kinematics is to describe the motion of the robotic system without consideration of the forces and torques causing the motion. This paper presents two methods to obtain the inverse kinematics of a mobile robot. In the first method, two rows of the forward kinematics are selected, the inverse of these two rows is obtained, and later the inverse matrix is combined with the third row of the forward kinematics. In the second method, the pseudo-inverse matrix of the forward kinematics matrix is obtained. The comparison result of the two proposed methods is presented. Two simulations show the effectiveness of the proposed inverse kinematics algorithm.

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

Access this article

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

Instant access to the full article PDF.

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
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References.

  1. Ge SS, Cui YJ (2000) New potential functions for mobile robot path planning. IEEE Trans Robotic Autom 16(5):615–620

    Google Scholar 

  2. Pathak K, Agrawal SK (2005) An integrated path-planning and control approach for nonholonomic unicycles using switched local potentials. IEEE Trans Robotic 21(6):1201–1208

    Google Scholar 

  3. Fan X, Luo X, Yi S, Yang S, Zhang H (2003) Optimal path planning for mobile robots based on intensified ant colony optimization algorithm. In: Proceedings of IEEE International Conference on Robotic Intelligent System, vol 1, pp 131–136

  4. Georgiev A, Allen PK (2004) Localization methods for a mobile robot in urban environments. IEEE Trans Robotic 20(5):851–864

    Google Scholar 

  5. Garulli A, Vicino A (2001) Set membership localization of mobile robots via angle measurements. IEEE Trans Robotic Autom 17(4):450–463

    Google Scholar 

  6. Liu HS, Pang KH (2001) Accelerometer for mobile robot positioning. IEEE Trans Ind Appl 37(3):812–819

    Google Scholar 

  7. Chwa D (2004) Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE Trans Control Syst Technol 12(4):637–644

    Google Scholar 

  8. Chang W-C, Lee S-A (2004) Autonomous vision-based pose control of mobile robots with tele-supervision. In: Proceedings of IEEE international conference on control applications, vol 2, pp 1049–1054

  9. Li T-H, Chang S-J, Tong W (2004) Fuzzy target tracking control of autonomous mobile robots by using infrared sensors. IEEE Trans Fuzzy Syst 12(4):491–501

    Google Scholar 

  10. Wei S, Zefran M (2005) Smooth path planning and control for mobile robots. In: IEEE Proceedings of network sensing and control, March 2005, pp 894–899

  11. Rubio JJ, García E, Pacheco J (2011) Trajectory planning and collisions detector for robotic arms, Neural Comput & Appl (online 2011)

  12. Christensen DJ, Campbell J, Stoy K (2010) Anatomy-based organization of morphology and control in self-reconfigurable modular robots. Neural Comput & Appl 19:787–805

    Article  Google Scholar 

  13. Chen C, Inoue Y, Shibata K (2011) Identification of a golf swing robot using soft computing approach. Neural Comput & Appl 20:729–740

    Article  Google Scholar 

  14. Villaverde I, Graña M (2011) Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera. Neural Comput & Appl 20:345–354

    Article  Google Scholar 

  15. Bachrach J, Beal J, McLurkin J (2010) Composable continuous-space programs for robotic swarms. Neural Comput & Appl 19:825–847

    Article  Google Scholar 

  16. Lee W-P, Yang T-H (2011) Combining GRN modeling and demonstration-based programming for robot control. Neural Comput & Appl 20:909–921

    Article  MathSciNet  Google Scholar 

  17. Chaoui H, Sicard P (2011) Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines. Neural Comput & Appl 20:717–727

    Article  Google Scholar 

  18. Chiu C-H (2010) Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control. Neural Comput & Appl 19:1153–1164

    Article  MathSciNet  Google Scholar 

  19. Wu Y, Sun F, Zheng J, Song Q (2010) A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system. Neural Comput & Appl 19:1013–1027

    Article  Google Scholar 

  20. Celikkanat H, Sahin E (2010) Steering self-organized robot flocks through externally guided individuals. Neural Comput & Appl 19:849–865

    Article  Google Scholar 

  21. Divelbiss AW, Wen JT (1997) Trajectory tracking control of a car- trailer system. IEEE Trans Automat Control 5(3):269–278

    Google Scholar 

  22. Aranda E, Salgado T, Velasco M, Control no lineal discontinuo de un robot movil, Computacion y Sistemas CIC- IPN

  23. Ollero BA, Robótica, manipuladores y robots móviles. Editorial Alfaomega. España

  24. Stanley I, Algebra lineal, Grupo Editorial Iberoamericana, México DF

  25. Ogata K, Ingeniería de control moderna. Editorial Prentice Hall, Segunda edición, México

  26. Kreyszig E, Matemáticas avanzadas para ingeniería. Tercera edición. Limusa Wiley

  27. Yue M, Hu P, Sun W (2010) Path following of a class of non-holonomic mobile robot with underactuated vehicle body. IET Control Theory Appl 4(10):1898–1904

    Article  MathSciNet  Google Scholar 

  28. Yoo SJ (2010) Adaptive tracking control for a class of wheeled mobile robots with unknown skidding and slipping. IET Control Theory Appl 4(10):2109–2119

    Article  MathSciNet  Google Scholar 

  29. Li Z, Zhang J, Yang Y (2010) Motion control of mobile under-actuated manipulators by implicit function using support vector machines. IET Control Theory Appl 4(11):2356–2368

    Article  MathSciNet  Google Scholar 

  30. Bi FY, Wei YJ, Zhang JZ, Cao W (2010) Position-tracking control of under-actuated autonomous underwater vehicles in the presence of unknown ocean currents. IET Control Theory Appl 4(11):2369–2380

    Article  MathSciNet  Google Scholar 

  31. Cho HC, Fadali MS, Lee KS, Kim NH (2010) Adaptive position and trajectory control of autonomous mobile robot systems with random friction. IET Control Theory Appl 4(12):2733–2742

    Article  MathSciNet  Google Scholar 

  32. Park BS, Park JB, Choi YH (2011) Robust adaptive formation control and collision avoidance for electrically driven non-holonomic mobile robots. IET Control Theory Appl 5(3):514–522

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors are grateful with the editor and with the reviewers for their valuable comments and insightful suggestions, which can help to improve this research significantly. The authors thank the Secretaria de Investigación y Posgrado and the Comisión de Operación y Fomento de Actividades Académicas del IPN and the Consejo Nacional de Ciencia y Tecnologia for their help in this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José de Jesús Rubio.

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Jesús Rubio, J., Aquino, V. & Figueroa, M. Inverse kinematics of a mobile robot. Neural Comput & Applic 23, 187–194 (2013). https://doi.org/10.1007/s00521-012-0854-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0854-0

Keywords

Navigation