Skip to main content

Using Growth Curve in Trajectory Planning for Industrial Manipulator

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8103))

Abstract

The paper presents a new algorithm to solve the problem of trajectory planning in industrial manipulator, the growth curve which is well known in Biological Sciences. The growth curve is used to demonstrate the relationship between the quantities of a certain creature over the time, which is similar to the curve of velocity in trajectory planning for industrial manipulator. This papers purpose is to introduce the algorithm and derive the logistic formula from basic growth curve to fit the velocity curve, using to plan the trajectory in industrial manipulator controlling. Although the algorithm is very simple and easy which contains only three parameters, the logistic curve can easily solves the general cases in trajectory planning where there are the upper limits of velocity and acceleration.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)

    Article  Google Scholar 

  2. Gasparetto, A., Zanotto, V.: Optimal trajectory planning for industrial robots. Advances in Engineering Software 41, 548–556 (2009)

    Article  Google Scholar 

  3. Xiao, Y., Du, Z., Dong, W.: Smooth and near time-optimal trajectory planning of industrial robots for online applications. Industrial Robot: An International Journal 39, 169–177 (2012)

    Article  Google Scholar 

  4. Sahar, G., Hollerbach, J.M.: Planning of Minimum- Time Trajectories for Robot Arms. The International Journal of Robotics Research 5, 90–100 (1986)

    Article  Google Scholar 

  5. Constantinescu, D., Croft, E.A.: Smooth and time-optimal trajectory planning for industrial manipulators along specified paths. Journal of Robotic Systems 17, 233–249 (2000)

    Article  MATH  Google Scholar 

  6. Liu, H., Lai, X., Wu, W.: Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints. Robotics and Computer-Integrated Manufacturing 29, 309–317 (2013)

    Article  Google Scholar 

  7. Huang, M.-S., Hsu, Y.-L., Fung, R.-F.: Minimum-Energy Point-to-Point Trajectory Planning. IEEE/ASME Transtractions on Mechatronics 17, 337–344 (2012)

    Article  Google Scholar 

  8. Gasparetto, A., Lanzutti, A., Vidoni, R., Zanotto, V.: Experimental validation and comparative analysis of optimal time-jerk algorithms for trajectory planning. Robotics and Computer-Integrated Manufacturing 28, 164–181 (2011)

    Article  Google Scholar 

  9. Roman-Roman, P., Torres-Ruiz, F.: Modelling logistic growth by a new diffusion process: application to biological systems. Biosystems 110, 9–21 (2012)

    Article  Google Scholar 

  10. Kwasnicki, W.: Logistic growth of the global economy and competitiveness of nations. Technological Forecasting and Social Change 80, 50–76 (2012)

    Article  Google Scholar 

  11. Tsoularis, A., Wallace, J.: Analysis of logistic growth models. Methematical Bilsciences 179, 21–55 (2002)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jiang, H., Yan, Y., Zhang, M., Zhang, J., Xu, J. (2013). Using Growth Curve in Trajectory Planning for Industrial Manipulator. In: Lee, J., Lee, M.C., Liu, H., Ryu, JH. (eds) Intelligent Robotics and Applications. ICIRA 2013. Lecture Notes in Computer Science(), vol 8103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40849-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40849-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40848-9

  • Online ISBN: 978-3-642-40849-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics