Robot Base Disturbance Optimization with Compact Differential Evolution Light
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.
KeywordsDistribution Algorithm Space Robot Memory Saving Compact Algorithm Exponential Crossover
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- 2.Cody, W.J.: Rational chebyshev approximations for the error function 23(107), 631–637 (1969)Google Scholar
- 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.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.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
- 7.Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer (2001)Google Scholar
- 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
- 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
- 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