Measurement planning is vital for automatic and digitalized aircraft assembly, and in particular, for the key execution sequences. In this article, a novel planning method that considers both order and efficiency is proposed. General rules are defined for the analysis of measurement tasks and their elements. A Laplace kernel function is then utilized for similarity quantification, and based on iterative two-step elementary transformations of the similarity matrix, the clustering of measurement tasks is achieved. A virtual task is proposed to bridge task clusters, and the preliminary sequences are obtained naturally. Given that the adjacent association results may not be the most efficient, octopus optimization is proposed to solve this special case of the traveling salesman problem. The measurement tasks and clusters are shops and cities, respectively. The octopus has multiple tentacles that traverse all shops, and each tentacle carries one feasible execution order. Experiments performed using aircraft inspection indicate that the proposed method can output multiple orderly measurement sequences. Compared with the preliminary sequences, the efficiency is obviously improved in terms of a decrease in the total measurement time. In addition, the optimal sequences are more even, and the duty ratio of the measurement device is decreased.

测量计划对于自动和数字化飞机组装至关重要，尤其是对于关键执行序列。在本文中，提出了一种兼顾顺序和效率的新型规划方法。为分析测量任务及其元素定义了通用规则。然后利用拉普拉斯核函数进行相似度量化，并基于相似度矩阵的迭代两步初等变换，实现测量任务的聚类。提出了一个虚拟任务来桥接任务集群，自然得到初步序列。鉴于相邻关联结果可能不是最有效的，章鱼优化被提出来解决旅行商问题的这种特殊情况。测量任务和集群是商店和城市，分别。章鱼有多个触手，遍历所有店铺，每个触手携带一个可行的执行指令。使用飞机检查进行的实验表明，所提出的方法可以输出多个有序的测量序列。与初步序列相比，在减少总测量时间方面效率明显提高。另外，最优序列更加均匀，降低了测量设备的占空比。在减少总测量时间方面效率明显提高。另外，最优序列更加均匀，降低了测量设备的占空比。在减少总测量时间方面效率明显提高。另外，最优序列更加均匀，降低了测量设备的占空比。