Robot Base Disturbance Optimization with Compact Differential Evolution Light

  • Giovanni Iacca
  • Fabio Caraffini
  • Ferrante Neri
  • Ernesto Mininno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

Despite the constant growth of the computational power in consumer electronics, very simple hardware is still used in space applications. In order to obtain the highest possible reliability, in space systems limited-power but fully tested and certified hardware is used, thus reducing fault risks. Some space applications require the solution of an optimization problem, often plagued by real-time and memory constraints. In this paper, the disturbance to the base of a robotic arm mounted on a spacecraft is modeled, and it is used as a cost function for an online trajectory optimization process. In order to tackle this problem in a computationally efficient manner, addressing not only the memory saving necessities but also real-time requirements, we propose a novel compact algorithm, namely compact Differential Evolution light (cDElight). cDElight belongs to the class of Estimation of Distribution Algorithms (EDAs), which mimic the behavior of population-based algorithms by means of a probabilistic model of the population of candidate solutions. This model has a more limited memory footprint than the actual population. Compared to a selected set of memory-saving algorithms, cDElight is able to obtain the best results, despite a lower computational overhead.

Keywords

Distribution Algorithm Space Robot Memory Saving Compact Algorithm Exponential Crossover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ahn, C.W., Ramakrishna, R.S.: Elitism based compact genetic algorithms. IEEE Transactions on Evolutionary Computation 7(4), 367–385 (2003)CrossRefGoogle Scholar
  2. 2.
    Cody, W.J.: Rational chebyshev approximations for the error function 23(107), 631–637 (1969)Google Scholar
  3. 3.
    Gautschi, W.: Error function and fresnel integrals. In: Abramowitz, M., Stegun, I.A. (eds.) Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, ch.7, pp. 297–309 (1972)Google Scholar
  4. 4.
    Huang, P., Chen, K., Xu, S.: Optimal path planning for minimizing disturbance of space robot. In: Proceedings of the IEEE International Conference on on Control, Automation, Robotics, and Vision (2006)Google Scholar
  5. 5.
    Iacca, G., Mallipeddi, R., Mininno, E., Neri, F., Suganthan, P.: Global supervision for compact differential evolution. In: Proceedings IEEE Symposium on Differential Evolution, pp. 25–32 (2011)Google Scholar
  6. 6.
    Iacca, G., Mininno, E., Neri, F.: Composed compact differential evolution. Evolutionary Intelligence 4(1), 17–29 (2011)CrossRefGoogle Scholar
  7. 7.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer (2001)Google Scholar
  8. 8.
    Mininno, E., Cupertino, F., Naso, D.: Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Transactions on Evolutionary Computation 12(2), 203–219 (2008)CrossRefGoogle Scholar
  9. 9.
    Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Transactions on Evolutionary Computation 15(1), 32–54 (2011)CrossRefGoogle Scholar
  10. 10.
    Neri, F., Iacca, G., Mininno, E.: Disturbed exploitation compact differential evolution for limited memory optimization problems. Information Sciences 181(12), 2469–2487 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Neri, F., Mininno, E.: Memetic compact differential evolution for cartesian robot control. IEEE Computational Intelligence Magazine 5(2), 54–65 (2010)CrossRefGoogle Scholar
  12. 12.
    Norman, P.G.: The new AP101S general-purpose computer (gpc) for the space shuttle. IEEE Proceedings 75, 308–319 (1987)CrossRefGoogle Scholar
  13. 13.
    Qin, A.K., Huang, V.L., Suganthan, P.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 13, 398–417 (2009)CrossRefGoogle Scholar
  14. 14.
    Ren, K., Fu, J.Z., Chen, Z.C.: A new linear interpolation method with lookahead for high speed machining. In: Technology and Innovation Conference, pp. 1056–1059 (2006)Google Scholar
  15. 15.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)CrossRefGoogle Scholar
  16. 16.
    Xinchao, Z.: Simulated annealing algorithm with adaptive neighborhood. Applied Soft Computing 11(2), 1827–1836 (2011)CrossRefGoogle Scholar
  17. 17.
    Xu, Y.: The measure of dynamic coupling of space robot system. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 615–620 (1993)Google Scholar
  18. 18.
    Zhang, J., Sanderson, A.C.: Jade: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)CrossRefGoogle Scholar
  19. 19.
    Zhou, J., Ji, Z., Shen, L.: Simplified intelligence single particle optimization based neural network for digit recognition. In: Proceedings of the Chinese Conference on Pattern Recognition (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Giovanni Iacca
    • 1
  • Fabio Caraffini
    • 1
  • Ferrante Neri
    • 1
  • Ernesto Mininno
    • 1
  1. 1.Department of Mathematical Information TechnologyUniversity of JyväskyläAgoraFinland

Personalised recommendations