Skip to main content

Advertisement

Springer Nature Link
Log in

Energy-saving trajectory planning for robots using the genetic algorithm with assistant chromosomes

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Fossil fuel depletion and global warming are becoming increasingly important problems. Many trajectory plans for robot manipulators are developed by prioritizing operation efficiency, such as operating time and controllability, without considering energy consumption. In this study, the energy consumption problem is examined. This study discusses the application of a genetic algorithm (GA) to solve the problem of minimizing the energy consumption of a robot manipulator with nonlinear friction in the joints. The GA can search a wide area for an optimal solution; however, a long computation time is required. A gradient method can be used to quickly find a solution; however, the solution has a high probability of being a local optimum. This paper proposes a method that combines a gradient method and GA to quickly determine an optimal solution. In addition, the validity of the proposed method is examined.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Harada K (2014) Optimization in robot motion planing. J Robot Soc Jpn 32(6):508–511

    Article  Google Scholar 

  2. Abe A, Sasamori K (2010) Optimal trajectory planning for a flexible manipulator. Trans Soc Instrum Control Eng 46(2):130–132

    Article  Google Scholar 

  3. Sato A, Sato O, Kono M, Kai K (2003) Trajectory for saving energy of direct-drive manipulator with two degree of freedom in FTP motion under gravity. Jpn Soc Precis Eng 69(9):1281–1285

    Article  Google Scholar 

  4. Abe A, Nemoto S (2012) An energy saving feedforward control technique for a 2-DOF flexible manipulator. Trans Jpn Soc Mech Eng C 78(789):1325–1337

    Article  Google Scholar 

  5. Izumi T (2000) Path planning for saving energy of a manipulator in PTP motions. J Robot Soc Jpn 18(7):972–978

    Article  Google Scholar 

  6. Izumi T (1995) Minimization of energy consumption for a manipulator with nonlinear friction in PTP motion. J Robot Soc Jpn 13(8):1179–1185

    Article  Google Scholar 

  7. Izumi T (2000) Path planning for saving energy of a manipulator in PTP motion. J Robot Soc Jpn 18(7):972–978

    Article  Google Scholar 

  8. Mohammed OA, Uler GF (1997) A hybrid technique for the optimal design of electromagnetic devices using direct search and genetic algorithms. IEEE Trans Magn 33(2):1931–1934

    Article  Google Scholar 

  9. Yokose Y, Izumi T (2003) Application of genetic algorithms for minimizing the consumption energy of a manipulator. Proc 8th Int Symp Artif Life Robot 1:50–53

    Google Scholar 

  10. Yokose Y (2017) Trajectory planning for a manipulator with nonlinear coulomb friction using a dynamically incremental genetic algorithm. J Artif Life Robot 22(1):31–35

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Science, National Institute of Technology, Kure College, 2-2-11 Agaminami, Kure, Hiroshima, 737-8506, Japan

    Yoshio Yokose

Authors
  1. Yoshio Yokose
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yoshio Yokose.

Additional information

Publisher's Note

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

This work was presented in part at the 24th International Symposium on Artificial Life and Robotics (Beppu, Oita, January 23–25, 2019).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yokose, Y. Energy-saving trajectory planning for robots using the genetic algorithm with assistant chromosomes. Artif Life Robotics 25, 89–93 (2020). https://doi.org/10.1007/s10015-019-00556-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-019-00556-8

Keywords