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This paper is represented to research of driving control for the forklift AGV. The related works that were studied about AGV as heavy equipment used two methods which are magnet-gyro and wire guidance for localization. However, they have weaknesses that are high cost, difficult maintenance according to change of environment. In this paper, we develop localization system through sensor fusion with laser navigation system and encoder, gyro for robustness. Also we design driving controller using fuzzy and proportional control. It considers distance and angle difference between forklift AGV and pallet for engaging work. To analyze performance of the proposed control system, we experiment in same working condition over 10 times. In the results, the average error was presented with 54.16mm between simulation of control navigation and real control navigation. Consequently, experimental result shows that the performance of proposed control system is effective.

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.

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This study describes the design and development of the novel model for the process optimization of solar cell fabrication. The model performance can affect the result of the physical experiment in the solar cell fabrication because the high accuracy model can provide the closer result to the output efficiency of the physical experiment. In this study, genetic programming (GP) based modeling technique was developed for the process simulation. GP is a global modeling technique, so it is suitable for process data modeling. This study describes the modified GP algorithm to solve the constant terminal problem. In the traditional GP, the constant term can be randomly selected within the fixed range when the structure is changed. Therefore, the variation ratio of the constant is too low to fit the model well. In this study, the novel GP is proposed. The method includes particle swarm optimization (PSO) to optimize the constant term in the terminals. PSO is a strong searching algorithm without a high computation cost. Actually, through the simulation results, the modeling performance and speed can be improved by the proposed GP. Because by the proposed modeling method, the structure and parameters of the model can be optimized simultaneously, the proposed method can be used as the new global modeling approach.