Automatic Adjustment For Laser Systems Using A Stochastic Binary Search Algorithm To Cope With Noisy Sensing Data

Nosato, Hirokazu; Murata, Nobuharu; Furuya, Tatsumi; Murakawa, Masahiro

doi:10.21307/ijssis-2017-305

Abstract

For laser systems, the adjustment of the optical axes is crucial. However, it is difficult for conventional methods to adjust the optical axes because they require high-precision positioning with μm resolutions and because laser systems have many adjustment points that have an interdependent relationship. We have proposed an automatic adjustment method using genetic algorithms to overcome this problem. However, there are still two problems that need to be solved: (1) long adjustment times, and (2) adjustment precision due to observational noise. In order to solve these problems, we propose a robust and efficient automatic adjustment method for the optical axes of laser systems using a binary search algorithm. Adjustment experiments for optical axes with 4-DOF demonstrate that the adjustment time could be reduced to half the conventional adjustment time with the genetic algorithm. Adjustment precision was enhanced by 60%

References

1M. Murakawa, T. Itatani, Y. Kasai, H. Yoshikawa and T. Higuchi, ‘‘An evolvable laser system for generating femtosecond pulses,’’ Proc. of the Second Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 636-642 (2000).
Search in Google Scholar Back to article
2H. Nosato, Y. Kasai, M. Murakawa, T. Itatani and T. Higuchi, ‘‘Automatic Adjustments of a Femtosecond-pulses Laser Using Genetic Algorithms,’’ Proc. of 2003 Congress on Evolutionary Computation (CEC 2003), pp. 2096-2102 (2003).
Search in Google Scholar Back to article
3N. Murata, H. Nosato, T. Furuya and M. Murakawa, ‘‘An Automatic Multi-objective Adjustment System for Optical Axes using Genetic Algorithms,’’ Proc. of 5th International Conference on Intelligent Systems Design and Applications (ISDA 2005), pp.546-551 (2005).10.1109/ISDA.2005.21
Search in Google Scholar Back to article
4E. J. Hughes, ‘‘Multi-objective Binary Search Optimization,’’ Proc. of Second International Conference on Evolutionary Multi-Criterion Optimization (EMO 2003), pp. 201-117 (2003).10.1007/3-540-36970-8_8
Search in Google Scholar Back to article
5J. M. Fitzpatrick and J. J. Grefenstette, ‘‘Genetic Algorithms in Noisy Environments,’’ Machine Learning 3, pp. 101-120 (1988).10.1007/BF00113893
Search in Google Scholar Back to article
6P. Stagge, ‘‘Averaging Efficiently in the Presence of Noise,’’ Proc. of Parallel Problem Solving from Nature (PPSN V), pp. 188-197 (1998).10.1007/BFb0056862
Search in Google Scholar Back to article
7H. Satoh, M. Yamamura and S. Kobayashi, ‘‘Minimal Generation Gap Model for Gas Considering Both Exploration and Exploitation,’’ Proc. of 4th International Conference on Soft Computing, pp.494-497 (1996).
Search in Google Scholar Back to article
8D. Whitley, K. Mathias, S. Rana and J. Dzubera, ‘‘Evaluation Evolutionary Algorithms,’’ Artificial Intelligence, Vol. 85, pp. 245-276 (1996).
Search in Google Scholar Back to article
9N. Metropolis, A. Rosenbluth, M. Rosenbulth, A. Teller and E. Teller, ‘‘Equation of State Calculation by Fast Computing Machines,’’ Vol. 21, Journ. of Chemical Physics, pp. 1087-1092 (1953).10.1063/1.1699114
Search in Google Scholar Back to article
10N. Murata, H. Nosato, T. Furuya and M. Murakawa, ‘‘Robust and Efficient Multiobjective Automatic Adjustment for Optical Axes in Laser Systems using Stochastic Binary Search Algorithm,’’ Proc. of the 7th International Conference on Evolvable Systems, LNCS, Vol. 4684, pp. 343-354, SPRINGER-VERLAG BERLIN (2007).10.1007/978-3-540-74626-3_33
Search in Google Scholar Back to article

Automatic Adjustment For Laser Systems Using A Stochastic Binary Search Algorithm To Cope With Noisy Sensing Data

Abstract

Paradigm

My account