基于改进隶属云模型蚁群算法的喷涂机器人喷枪轨迹组合优化  

Combinatorial Optimization of Spray Painting Robot Tool Trajectory Based on Improved Membership Cloud Models Ant Colony Algorithm

作　　者：李翠明[1] 龚俊[1] 牛万才[1] 王翀[1] 

机构地区：[1]兰州理工大学机电工程学院,兰州730050

出　　处：《上海交通大学学报》2015年第3期387-391,共5页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金项目(51165022);甘肃省自然科学基金项目(145RJZA028)资助

摘　　要：针对复杂曲面分片后的喷枪轨迹组合优化问题,利用哈密尔顿图将其转化为广义开环旅行商问题(OTSP),采用"问题无关的进化算法与问题相关的局部搜索相结合"的策略,首先引入隶属云模型来自适应调节蚁群算法中控制的随机性,然后引入K-opt局部搜索策略的基于改进隶属云模型蚁群算法(MCMACA)对喷枪轨迹组合优化的OTSP问题进行求解.仿真结果表明,改进隶属云模型蚁群算法的全局搜索性和局部收敛性更佳,在复杂曲面上对喷涂机器人喷枪轨迹进行优化具有明显的优越性.Aimed at the combinatorial optimization of tool trajectory upon the complex free-form curved surface piece, Hamiltonian path was adopted to transform it to an open traveling salesman problem （OTSP）. Meanwhile the strategy of integrating the problem-unrelated optimization algorithm and the problem-related local search was adopted. First, the membership cloud models were introduced to adapt and adjust the randomness controlled by the ant colony algorithm. Then, the K-opt partial search strategy was introduced to find the solution to OTSP in respect of the combinatorial optimization of the tool trajec- tory based on the improved membership cloud models ant colony algorithm （MCMACA）. The simulation result shows that MCMACA features better global search ability and local convergence. Meanwhile, it has obvious advantages in terms of optimization of the spray painting robot tool trajectory on complex curved surface.

关 键 词：组合优化 喷涂机器人 蚁群算法 隶属云模型 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]