直纹曲面喷漆机器人喷枪轨迹多目标优化  

Multi-Objective Trajectory Optimization of Spray Painting Robot for Ruled Surfaces

作　　者：马淑梅[1] 谢涛[1] 李爱平[1] 杨连生[1,2] 

机构地区：[1]同济大学机械与能源工程学院,上海201804 [2]上海外高桥造船有限公司,上海200137

出　　处：《同济大学学报（自然科学版）》2018年第3期359-367,共9页Journal of Tongji University:Natural Science

基　　金：上海市经济和信息化委员会资助项目(沪CXY-2013-25);上海市科学技术委员会资助项目(14111104400)

摘　　要：针对直纹曲面上喷漆机器人的喷枪轨迹多目标优化问题,通过平面喷漆实验,采集各点膜厚数据,运用MATLAB遗传算法工具箱拟合β分布,建立直纹曲面漆膜厚度生长模型.将曲面离散为点集,采用三次B样条曲线拟合生成初始喷枪轨迹.以曲面上各点漆膜厚度均匀和喷涂效率高为目标建立喷枪轨迹多目标优化模型,并采用改进的快速非支配排序遗传算法对该模型求解,获得喷枪轨迹最优解集,最终实现了直纹曲面喷枪轨迹的优化目标.通过实例结果对比验证了该方法的有效性和实用性.To solve the problem of multi-objective tool trajectory of painting robot for ruled surface the fitting film thickness data were collected from the planar painting experiment. By using the MATLAB genetic algorithm toolbox, theβ distribution was obtained. The paint thickness growth model for ruled surface was established based on the planar paint thickness distribution. The surface was discretized into a set of points, and the tool path was fitted by the cubic B spline curve. A multi-objective optimization model was built aimed to get uniform paint thickness on curved surface and high painting efficiency, and an improved fast and elitist non-dominated sorting genetic algorithm （NSGA- Ⅱ ） was applied to solve the model. A serial of trajectory optimization schemes were presented. Finally, the trajectory optimization purpose on ruled surface was realized. The case result was compared to verify the effectiveness and practicability of the proposed method.

关 键 词：多目标优化 喷漆机器人 直纹曲面 快速非支配排序遗传算法 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]