This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR) and summarizes the status quo of applying artificial intelligence (AI) techniques. We first analyze the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu, and the curriculum. Then, we transform the relationship model into the perception-planning-action model to explore what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting a human-robot teaching-learning process and AI-assisted educational techniques, which illustrates the design guidelines and future research perspectives in intelligent tutoring robots.

Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.

With the explosion of intelligent network applications and the prosperity of artificial intelligence (AI) technologies, the sixth generation (6G) wireless networks are not only expected to further improve network capacity, but also anticipated to establish a new architecture of “Intelligent Connectivity of Everything”. Semantic communication (SC) is a promising solution for future 6G networks due to its natural capability of integrating application requirements and information meaning into data transmission processes. In this paper, a comprehensive survey that overviews how SC can be applied for 6G networks and the key technologies of SC is presented. For this purpose, we first provide a detailed overview of the concepts of semantic information (SI) and SC, as well as the classifications of SC. Then, we present the vision of mutual support between SC technologies and 6G networks, as well as the potential benefits using SC for 6G applications. To achieve the benefits of SC, the fundamental theories and four important technologies in SC are thoroughly investigated, which are SC system architecture design, SI extraction, SI transmission, and SC performance evaluation. Then, we introduce open problems and potential research directions pertaining to SC. In a nutshell, this paper provides a holistic review of concepts, applications, fundamentals, key technologies, existing challenges, and open issues of SC tailored to the requirements of 6G networks.

This paper proposes an efficient and robust Loop closure detection (LCD) method based on Convolutional neural network (CNN) feature. The primary method is called SeqCNNSLAM, in which both the outputs of the intermediate layer of a pre-trained CNN and the outputs of traditional sequence-based matching procedure are incorporated, making it possible to handle the viewpoint and condition variance properly. An acceleration algorithm for SeqCNNSLAM is developed to reduce the search range for the current image, resulting in a new LCD method called A-SeqCNNSLAM. To improve the applicability of A-SeqCNNSLAM to new environments, O-SeqCNNSLAM is proposed for online parameters adjustment in A-SeqCNNSLAM. In addition to the above work, we further put forward a promising idea to enhance SeqSLAM by integrating the both CNN features and VLAD's advantages called patch based SeqCNNSLAM (P-SeqCNNSLAM), and provide some preliminary experimental results to reveal its performance.

The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios.