This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low efficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The performance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further analysis of the proposed SSD neural network through comparison with other neural networks suggests that its feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later promotion and adoption of mobile robots in various fields.

本工作旨在探索计算机智能算法和机器视觉下的机器人自动导航模型，使移动机器人更好地服务于各行各业。针对目前智能机器人成本高、工作灵活性差的现状，本工作对图像处理算法进行了创新性研究和改进。和控制算法。在导航线边缘检测阶段，针对传统蚁群算法效率低的问题，结合Canny算法对其进行改进，提出一种基于Canny的蚁群算法进行轨迹边缘检测。此外，采用 Single Shot MultiBox Detector (SSD) 算法检测机器人导航轨迹中的障碍物。通过仿真分析性能。结果表明，本文提出的基于Canny的蚁群算法的导航准确率基本稳定在89.62%，运行时间最短。进一步分析提出的SSD神经网络通过与其他神经网络的比较表明其特征识别准确率达到了92.90%。准确率至少比其他神经网络算法高3.74%，运行时间稳定在37.99 s左右，丢包率接近0。因此，所构建的移动机器人自动导航模型在保证错误的前提。此外，数据传输效果理想。可为后期移动机器人在各个领域的推广和采用提供实验依据。