The surface quality inspection of industrial printed circuit board (PCB) is a vitally important link in its manufacturing process. To inspect surface defects of PCBs effectively, the automatic optical inspection (AOI) technology, in which the PCB image acquisition depends on the path planning method, is widely adopted by industry. It is regarded as a characteristic travelling salesman problem (TSP), which includes component clustering, location adjustment and algorithm adaptation optimization. In this paper, by improving the ant colony algorithm (ACA) algorithm, we devise a PCB image acquisition path planning model and the corresponding solving algorithms. Because the ACA encounters difficulty escaping from the local optimal solution, an improved ACA with a negative feedback mechanism is proposed that is able to obtain a better tour path with a higher probability. Aiming at the uncertainty of the local location of image acquisition windows, location adjustment methods are introduced to further shorten the path length and improve the image acquisition efficiency. Finally, via simulation experiments, the proposed global negative feedback ACA (GNF-ACA) can shorten the average length of the tour path by 1.7% without changing the time complexity. The three methods of location adjustment can further shorten the length of the tour path by 5.6%, 13.1% and 13.7%.

工业印刷电路板（PCB）的表面质量检查是其制造过程中至关重要的环节。为了有效地检查PCB的表面缺陷，行业广泛采用了自动光学检查（AOI）技术，该技术中的PCB图像获取取决于路径规划方法。它被视为特征性旅行商问题（TSP），其中包括组件聚类，位置调整和算法自适应优化。本文通过改进蚁群算法（ACA），设计了一种PCB图像采集路径规划模型及相应的求解算法。由于ACA难以摆脱局部最优解，提出了一种带有负反馈机制的改进型ACA，它能够以更高的概率获得更好的游览路径。针对图像采集窗口局部位置的不确定性，提出了位置调整方法，以进一步缩短路径长度，提高图像采集效率。最后，通过仿真实验，提出的全局负反馈ACA（GNF-ACA）可以将游览路径的平均长度缩短1.7％，而不会更改时间复杂度。三种位置调整方法可以进一步缩短游览路线的长度5.6％，13.1％和13.7％。通过仿真实验，提出的全局负反馈ACA（GNF-ACA）可以将游览路径的平均长度缩短1.7％，而不会更改时间复杂度。三种位置调整方法可以进一步将游览路径的长度缩短5.6％，13.1％和13.7％。通过模拟实验，提出的全局负反馈ACA（GNF-ACA）可以将游览路径的平均长度缩短1.7％，而不会更改时间复杂度。三种位置调整方法可以进一步将游览路径的长度缩短5.6％，13.1％和13.7％。