基于多因子量化选股模型的采摘机器人学习策略研究

Study on Learning Strategy of Picking Robot Based on Multi Factor Quantitative Stock Selection Model

首先,基于多因子量化选股模型制定了采摘机器人学习策略;然后,利用多因子量化选股模型对蚁群算法进行了改进,并利用改进的蚁群算法对采摘机器人的路径规划进行了分析与研究。MatLab实验结果表明:此算法为采摘机器人规划了一条长度最短、转弯次数最少的最优路径,移动过程中没有发生碰撞,证明算法对改善采摘机器人移动路径效果明显,使规划的路径更加合理。

It first analyzes the multi factor quantitative stock selection model,builds the model of the picking robot′s learning strategy,then improves the ant colony algorithm by using the multi factor quantitative stock selection model,and analyzes and studies the path planning of the picking robot by using the improved ant colony algorithm.The results of Matlab experiments show that the algorithm has planned an optimal path with the shortest length and the least number of turns for the picking robot,and there is no collision during the moving process,which proves that the algorithm has obvious effect on improving the moving path of the picking robot and makes the planned path more reasonable.