We have proposed an automatic adjustment method using genetic algorithms (GA) to adjust the optical axes in laser systems. However, there are still two tasks that need to be solved: (1) long adjustment time and (2) adjustment precision due to observation noise. In order to solve these tasks, we propose a robust and efficient automatic adjustment method for the optical axes of laser systems using stochastic binary search algorithm. Adjustment experiments for optical axes with 4- DOF demonstrate that the adjustment time could be reduced to half of conventional adjustment time with GA. Adjustment precision was enhanced by 60%.
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References
M. Murakawa, T. Itatani, Y. Kasai, H. Yoshikawa, and T. Higuchi. An evolvable laser system for generating femtosecond pulses. Proceedings of the Second Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, pages 636-642, (2000).
H. Nosato, Y. Kasai, M. Murakawa, T. Itatani, and T. Higuchi. Automatic adjustments of a femtosecond-pulses laser using genetic algorithms. Proceedings of 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, pages 2096-2101, (2003).
N. Murata, H. Nosato, T. Furuya, and M. Murakawa. An automatic multiobjective adjustment system for optical axes using genetic algorithms. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA 2005), Wroclaw, Poland, pages 546-551, (2005).
E.J. Hughes. Multi-objective binary search optimisation. Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimisation (EMO 2003), Faro, Portugal, pages 102-117, (2003).
N. Murata, H. Nosato, T. Furuya, and M. Murakawa. Robust and Efficient Automatic Adjustment for Optical Axes in Laser Systems using Stochastic Binary Search Algorithm for Noisy Environments Proceedings of the 3rd International Conference on Autonomous Robots and Agents (ICARA 2006), Palmerston North, New Zealand, pages 261-266, (2006).
J.M. Fitzpatrick and J.J. Greffenstette. Genetic algorithms in noisy environments. Machine Learning, 3:101-120, (1988).
P. Stagge. Averaging efficiently in the presence of noise. Proceedings of Parallel Problem Solving from Nature (PPSN V), Amsterdam, pages 188-197, (1998).
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Murata, N., Nosato, H., Furuya, T., Murakawa, M. (2007). Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm for Noisy Environments. In: Mukhopadhyay, S.C., Gupta, G.S. (eds) Autonomous Robots and Agents. Studies in Computational Intelligence, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73424-6_16
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