Exploration on Target Object Recognition and Location Technology of Mobile Robot Based on Digital Twin

In recent years, robot technology has developed rapidly. With the continuous progress and innovation of technology, intelligent mobile robots have become a very common technical element in life. Mobile robot target recognition is a multidisciplinary and complex technology based on image processing, analysis, and understanding, which has great application value and importance. Digital Twin technology provides services, data, and information and physical space can be integrated and displayed through the connection of analog models and physical devices. Therefore, there is an urgent need to study the concept of digital twins and combine them with digital robots to identify and identify targets. In this paper, digital twinning technology was applied to mobile robots to study target recognition and positioning technology. This article constructed a mobile robot digital twin system, and introduced the interactive control framework of the digital twin mobile robot and the virtual real interaction mechanism. The overall framework and operation mechanism of the mobile robot digital twin system have been basically established. After taking photos of the target, the minimum variance filtering algorithm was used for image processing. The threshold segmentation algorithm was used to segment the processed image, and then the directional gradient histogram method was used to extract image features. Finally, SVM (Support Vector Machine) classifier was used to classify images based on the extracted image features, so as to achieve the goal of target object recognition. After completing target object recognition, EKF (Extended Kalman Filter) was used to locate the target object, thereby achieving target object positioning. The experimental part carried out target object recognition and positioning. The experimental results showed that the digital twin mobile robot had a high effect on target object recognition and positioning, with an accuracy rate of over 80% for target object recognition. The x-axis and y-axis positioning errors of the digital twin mobile robot were lower than traditional positioning algorithms, and could effectively identify and locate target objects.