CAJ | 학술논문

探讨一种基于PSO/GA （粒子群优化/遗传算法）混合算法的立体视觉测量系统优化布局方法。以空间点目标的三维重构不确定度最小为目标，构建了立体视觉测量系统优化布局的目标函数。通过实例阐述了使用PSO/GA混合算法求解系统最优布局参数的过程，并且在MATLAB环境下对该方法做了验证。仿真实验结果表明：与传统的粒子群优化算法和遗传算法相比，使用PSO/GA混合算法得到的最佳个体适应度曲线上升速度最快，而且求得的系统最优布局参数使空间点目标的三维重构不确定度最小。
탐토일충기우PSO/GA （입자군우화/유전산법）혼합산법적입체시각측량계통우화포국방법。이공간점목표적삼유중구불학정도최소위목표，구건료입체시각측량계통우화포국적목표함수。통과실례천술료사용PSO/GA혼합산법구해계통최우포국삼수적과정，병차재MATLAB배경하대해방법주료험증。방진실험결과표명：여전통적입자군우화산법화유전산법상비，사용PSO/GA혼합산법득도적최가개체괄응도곡선상승속도최쾌，이차구득적계통최우포국삼수사공간점목표적삼유중구불학정도최소。
A PSO/GA (particle swarm optimization/genetic algorithm)based method for optimal placement of a stereo-vision measurement system was discussed. With the goal of finding the optimal variables to minimize the uncertainties associated with the re-construction of 3D points,an objective function for optimal placement of a stereo-vision measurement system was constructed. The pro-cedure for solving the optimal parameters of a stereo-vision measurement system was described,and the proposed method was verified using MATLAB. The simulation results show that the curve of best-of-generation fitness value obtained by PSO/GA hybrid algorithm raises more quickly than those obtained by the traditional particle swarm optimization and genetic algorithm,and the optimal solutions minimize the uncertainties associated with the reconstruction of 3D points.