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.
This research is supported by the Academy of Finland, Akatemiatutkija 130600, Algorithmic Design Issues in Memetic Computing and Tutkijatohtori 140487, Algorithmic Design and Software Implementation: a Novel Optimization Platform.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahn, C.W., Ramakrishna, R.S.: Elitism based compact genetic algorithms. IEEE Transactions on Evolutionary Computation 7(4), 367–385 (2003)
Cody, W.J.: Rational chebyshev approximations for the error function 23(107), 631–637 (1969)
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)
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)
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)
Iacca, G., Mininno, E., Neri, F.: Composed compact differential evolution. Evolutionary Intelligence 4(1), 17–29 (2011)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer (2001)
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)
Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Transactions on Evolutionary Computation 15(1), 32–54 (2011)
Neri, F., Iacca, G., Mininno, E.: Disturbed exploitation compact differential evolution for limited memory optimization problems. Information Sciences 181(12), 2469–2487 (2011)
Neri, F., Mininno, E.: Memetic compact differential evolution for cartesian robot control. IEEE Computational Intelligence Magazine 5(2), 54–65 (2010)
Norman, P.G.: The new AP101S general-purpose computer (gpc) for the space shuttle. IEEE Proceedings 75, 308–319 (1987)
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)
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)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)
Xinchao, Z.: Simulated annealing algorithm with adaptive neighborhood. Applied Soft Computing 11(2), 1827–1836 (2011)
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)
Zhang, J., Sanderson, A.C.: Jade: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iacca, G., Caraffini, F., Neri, F., Mininno, E. (2012). Robot Base Disturbance Optimization with Compact Differential Evolution Light. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-29178-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
eBook Packages: Computer ScienceComputer Science (R0)