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Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

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In this brief, a fast robust integrated guidance and control design approach, considering the 3-D interception of interceptors for hypersonic vehicles, is proposed. The proposed method is designed using the modified fast terminal sliding mode control and dynamic surface control with signal compensation. The fractional integral fast terminal sliding functions are presented independently, and all the sliding phases run parallel to increase the system response speed. Utilizing the dynamic surface control method, the robust compensation signals are constructed to eliminate the influence of uncertainty equivalents thoroughly. Therefore, the proposed approach guarantees the fast convergence of line-of-sight angular rates and the system robustness for uncertainties. The simulation results demonstrate the robustness of the proposed controller, and a variety of comparison studies are also carried out to demonstrate the advantage of the new approach.Index Terms-Dynamic surface control, fast terminal sliding mode (FTSM) control, integrated guidance and control (IGC), interceptor.

Introduction Breast cancer, one of the most common health threats to females worldwide, has always been a crucial topic in the medical field. With the rapid development of digital pathology, many scholars have used AI-based systems to classify breast cancer pathological images. However, most existing studies only stayed on the binary classification of breast lesions (normal vs tumor or benign vs malignant), far from meeting the clinical demand. Therefore, we established a multi-class classification system of breast digital pathology images based on AI, which is more clinically practical than the binary classification system. Methods In this paper, we adopted a two-stage architecture based on deep learning method and machine learning method for the multi-class classification (normal tissue, benign lesion, ductal carcinoma in situ, and invasive carcinoma) of breast digital pathological images. Results The proposed approach achieved an overall accuracy of 86.67% at patch-level. At WSI-level, the overall accuracies of our classification system were 88.16% on validation data and 90.43% on test data. Additionally, we used two public datasets, the BreakHis and BACH, for independent verification. The accuracies our model obtained on these two datasets were comparable to related publications. Furthermore, our model could achieve accuracies of 85.19% on multi-classification and 96.30% on binary classification (non-malignant vs malignant) using pathology images of frozen sections, which was proven to have good generalizability. Then, we used t-SNE for visualization of patch classification efficiency. Finally, we analyzed morphological characteristics of patches learned by the model. Conclusion The proposed two-stage model could be effectively applied to the multi-class classification task of breast pathology images and could be a very useful tool for assisting pathologists in diagnosing breast cancer.