Advertisement

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

  • Yoshio YokoseEmail author
Original Article

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.

Keywords

Robot manipulator Genetic algorithm Two-point boundary value problem Consumption energy 

Notes

References

  1. 1.
    Harada K (2014) Optimization in robot motion planing. J Robot Soc Jpn 32(6):508–511CrossRefGoogle Scholar
  2. 2.
    Abe A, Sasamori K (2010) Optimal trajectory planning for a flexible manipulator. Trans Soc Instrum Control Eng 46(2):130–132CrossRefGoogle Scholar
  3. 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):1281–1285CrossRefGoogle Scholar
  4. 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):1325–1337CrossRefGoogle Scholar
  5. 5.
    Izumi T (2000) Path planning for saving energy of a manipulator in PTP motions. J Robot Soc Jpn 18(7):972–978CrossRefGoogle Scholar
  6. 6.
    Izumi T (1995) Minimization of energy consumption for a manipulator with nonlinear friction in PTP motion. J Robot Soc Jpn 13(8):1179–1185CrossRefGoogle Scholar
  7. 7.
    Izumi T (2000) Path planning for saving energy of a manipulator in PTP motion. J Robot Soc Jpn 18(7):972–978CrossRefGoogle Scholar
  8. 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):1931–1934CrossRefGoogle Scholar
  9. 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 1:50–53Google Scholar
  10. 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):31–35CrossRefGoogle Scholar

Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information ScienceNational Institute of Technology, Kure CollegeHiroshimaJapan

Personalised recommendations