基于离散帝王蝶算法的喷涂路径组合优化

Spray Path Combination Optimization Based on Discrete Monarch Butterfly Optimization

针对复杂曲面分片后喷涂机器人喷枪路径排序和组合过程中存在的效率低和路径碰撞的问题,提出了一种适用于求解组合优化问题的离散帝王蝶算法,并应用于求解喷枪路径的排序组合问题中。为了构建问题模型,将喷枪路径的排序和组合问题抽象为路径点的开环广义旅行商问题,并建立了该问题的最短路径模型和碰撞模型。同时,为了对传统帝王蝶算法进行改进,采用了一种具有三层结构的矩阵编码方法来解决种群个体编码问题,提出了一种基于贪婪算法的种群初始化方法和一种基于模拟退火算法的种群更新策略来提高算法的收敛速度和精度。在仿真实验中,相较于遗传算法和粒子群算法,所提出的算法的平均迭代次数分别减小了32.3%和21.0%,所规划出的路径碰撞次数降低为0。实验结果表明,所提出的算法能够有效地提高喷涂机器人的喷涂效率和喷涂路径的安全性。

A discrete monarch butterfly optimization for solving combinatorial optimization problem is presented to solve the inefficiency and path collision in the process of path ordering and combination of spray guns of a robot after splitting a complex surface.In order to build the problem model, the problem of sorting and combining the airbrush paths is abstracted as an open-loop generalized traveling salesman problem of path points, and the shortest path model and collision model of the problem are established.In addition, in order to improve the traditional monarch butterfly optimization, a matrix coding method with three layers structure is used to solve the population individual coding problem.A population initialization method based on greedy algorithm and a population update strategy based on simulated annealing algorithm are presented to improve the convergence speed and accuracy of the algorithm.In the simulation, the average number of iterations of the proposed algorithm is reduced by 32.3% and 21.0% respectively, compared with the genetic algorithm and the particle swarm algorithm, and the number of collisions of the planned path is reduced to 0.The experimental results show that the proposed algorithm can effectively improve the spraying efficiency and the safety of the spraying path of the spraying robot.