Skip to main content

Robot Kinematics and Dynamics Modeling

  • Chapter
  • First Online:
Advanced Technologies in Modern Robotic Applications

Abstract

The robotic kinematics is essential for describing an end-effector’s position, orientation as well as motion of all the joints, while dynamics modeling is crucial for analyzing and synthesizing the dynamic behavior of robot. In this chapter, the kinematics and dynamics modeling procedures of the Baxter robot are investigated thoroughly. The robotic kinematics is briefly reviewed by highlighting its basic role in analyzing the motion of robot. By extracting the parameters from an URDF file, the kinematics model of the Baxter robot is built. Two experiments are performed to verify that the kinematics model matches the real robot. Next, the dynamics of robot is briefly introduced by highlighting its role in establishing the relation between the joint actuator torques and the resulting motion. The method for derivation of the Lagrange–Euler dynamics of the Baxter manipulator is presented, followed by experimental verification using data collected from the physical robot. The results show that the derived dynamics model is a good match to the real dynamics, with small errors in three different end-effector trajectories.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Denavit, J.: A kinematic notation for lower-pair mechanisms based on matrices. ASME J. Appl. Mech. 22, 215–221 (1955)

    MathSciNet  MATH  Google Scholar 

  2. Nethery, J.F., Spong, M.W.: Robotica: a mathematica package for robot analysis. Robot. Autom. Mag. IEEE 1(1), 13–20 (1994)

    Article  Google Scholar 

  3. Corke, P.: A robotics toolbox for matlab. Robot. Autom. Mag. IEEE 3(1), 24–32 (1996)

    Article  Google Scholar 

  4. Corke, P.: A simple and systematic approach to assigning denavit-hartenberg parameters. IEEE Trans. Robot. 23(3), 590–594 (2007)

    Article  Google Scholar 

  5. Ju, Z., Yang, C., Ma, H.: Kinematics modeling and experimental verification of baxter robot, Chinese Control Conference (CCC). 33, 8518–8523 (2014)

    Google Scholar 

  6. About ROS: http://www.ros.org/about-ros/

  7. Martinez, A., Fernández, E.: Learning ROS for Robotics Programming. Packt Publishing Ltd, Birmingham (2013)

    Google Scholar 

  8. RethinkRobotics baxter common: https://github.com/RethinkRobotics/baxter_common/tree/master/baxter_description

  9. Tuck Arms Example: https://github.com/RethinkRobotics/sdk-docs/wiki/Tuck-Arms-Example

  10. Uebel, M., Minis, I., Cleary, K.: Improved computed torque control for industrial robots,“ In: Proceedings of the IEEE International on Conference Robotics and Automation, pp. 528–533 (1992)

    Google Scholar 

  11. Poignet, P., Gautier, M.: Nonlinear model predictive control of a robot manipulator. In: Proceedings of the 6th IEEE International Workshop on Advanced Motion Control, pp. 401–406. (2000)

    Google Scholar 

  12. Feng, Y., Yu, X., Man, Z.: Non-singular terminal sliding mode control of rigid manipulators. Automatica 38(12), 2159–2167 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  13. Fu, K., Gonzalez, C.R.C.: Robotics Control, Sensing, Vision, and, Intelligence. McGraw-Hill, New York (1987)

    Google Scholar 

  14. Bejczy, A.K.: Robot Arm Dynamics and Control, pp. 33–669. Jet Propulsion Laboratory Technical Memo, Pasadena (1974)

    Google Scholar 

  15. Bejczy, A., Paul, R.: Simplified robot arm dynamics for control. In: IEEE 20th Conference on Decision and Control including the Symposium on Adaptive Processes (1981)

    Google Scholar 

  16. Megahed, S.M.: Principles of Robot Modelling and Simulation. Wiley, Hoboken (1993)

    Google Scholar 

  17. Lee, C., Lee, B., Nigam, R.: Development of the generalized d’alembert equations of motion for mechanical manipulators. In: 22nd IEEE Conference on Decision and Control, pp. 1205–1210 (1983)

    Google Scholar 

  18. Hollerbach, J.M.: A recursive lagrangian formulation of maniputator dynamics and a comparative study of dynamics formulation complexity. IEEE Trans. Syst. Man Cybern. 10(11), 730–736 (1980)

    Article  MathSciNet  Google Scholar 

  19. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling, Planning and Control. Springer, Heidelberg (2009)

    Google Scholar 

  20. Mckerrow, P.: Introduction to Robotics. Addison-Wesley Longman Publishing, Boston (1991)

    Google Scholar 

  21. Ju, Z., Yang, C., Ma, H.: Kinematic modeling and experimental verification of Baxter robot. In: Proceedings of the 33rd Chinese Control Conference Nanjing, China, pp. 8518–8523. 28–30 July 2014

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Chenguang Yang or Hongbin Ma .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Science Press and Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Yang, C., Ma, H., Fu, M. (2016). Robot Kinematics and Dynamics Modeling. In: Advanced Technologies in Modern Robotic Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-0830-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0830-6_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0829-0

  • Online ISBN: 978-981-10-0830-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics