船舶外板喷涂机器人多层喷涂轨迹优化研究  

Research on Optimization of Multi-layer Trajectory for Ship Outer Plate Painting Robot

作　　者：马淑梅[1] 谢涛[1] 周云中 李爱平[1] 杨连生[1] 

机构地区：[1]同济大学机械与能源工程学院,上海201804

出　　处：《机电一体化》2017年第8期23-30,共8页Mechatronics

基　　金：上海经信委资助项目(沪CXY-2013-25);上海科委资助项目(14111104400)

摘　　要：针对船舶外板喷涂机器人多层喷涂轨迹优化问题,以提高喷涂质量和效率为目标,提出一种平面多层喷涂轨迹优化方法。根据平面单层膜厚分布,推导建立平面多层漆膜分布模型,并采用快速非支配排序遗传算法(Non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)优化各层喷涂轨迹。通过对船舶外板进行多层仿真喷涂,与未优化的喷涂轨迹比较,提高了漆膜均匀性和喷涂效率,验证了轨迹优化方法的合理性;与传统遗传算法优化对比,结果验证了NSGA-Ⅱ算法的有效性。To solve the problem of optimizing multi-layer trajectory for ship outer plate painting robot, a planar multi-layer spray trajectory optimization method was proposed based on the purpose of improving painting quality and efficiency. The multi-layer paint thickness distribution model was deduced from the paint distribution for one layer on the planar, and the non-dominated sorting genetic algorithm Ⅱ （ NSGA - Ⅱ） was used to optimize the spraying trajectory. Compared with the un-optimized spraying trajectories, the coating uniformity and the spraying efficiency are improved by the multi-layer simulation spraying of the ship outer plate, and the rationality of the trajectory optimization method is verified. Compared with the traditional genetic algorithm, The effectiveness of NSGA - Ⅱ algorithm was proved.

关 键 词：船舶外板 喷涂机器人 轨迹优化 NSGA-Ⅱ算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]