基于改进灰狼算法优化SVM的机器人坡口类型识别

Robot weld type recognition based on improved grey wolf algorithm optimizing SVM

基于双目视觉传感器的机器人移动平台建立图像采集系统,研究了一种改进的灰狼算法和最小化参数策略结合,来优化支持向量机,实现对不同焊缝类型进行识别。首先,在灰狼算法中引入佳点集理论生成初始种群,减少灰狼种群种类数,为算法全局搜索的快捷和稳定性奠定基础。然后,在分类器SVM中引入非线性收敛因子,并结合最小化参数的策略,加强最优参数的泛化能力。最后,通过基于最优参数建立的SVM模型进行焊缝类型识别试验。证明了改进算法优化的SVM模型相对于粒子群算法、遗传算法、布谷鸟算法和基本灰狼算法,在识别准确率和优化速度方面都有了较大的提升。

An image acquisition system based on binocular visual sensors for robot mobile platforms was established,and an improved grey wolf algorithm combined with a minimization parameter strategy was studied to optimize support vector machines and achieve recognition of different weld types.Firstly,introducing the theory of the best point set into the Grey Wolf algorithm to generate an initial population,reducing the number of species in the Grey Wolf population and laying the foundation for the fast and stable global search of the algorithm.Then,a nonlinear convergence factor was introduced into the classifier SVM and combined with a strategy of minimizing parameters to enhance the generalization ability of the optimal parameters.Finally,an SVM model based on the optimal parameters was used for weld seam type recognition experiments.It is proved that the improved algorithm optimized SVM model has significantly improved recognition accuracy and optimization speed compared to particle swarm optimization,genetic algorithm,cuckoo bird algorithm,and basic grey wolf algorithm.