The integration of virtual and real environments is driving transformative advancements in unmanned systems, offering new avenues for intelligent collaboration and adaptive deployment. This survey presents a comprehensive framework for virtual-real integration centered on unmanned systems, aiming to unify fragmented research efforts and guide future exploration. By adopting both global and local perspectives, the framework facilitates a cohesive understanding of system components, their interactions, and the structural relationships that underpin seamless coordination. This is further complemented by an application case study and a synthesis of the current limitations. To operationalize this framework, the paper systematically examines enabling technologies, existing constraints, and the collaborative architecture that supports dynamic interaction across physical and virtual domains. It further outlines the evolving trends and construction requirements of practical application scenarios. Finally, key challenges and emerging research opportunities are discussed to inform future work and encourage deeper exploration of this rapidly developing field.

Leveraging large language models (LLMs) for collaborative sequential decision-making is a significant challenge, despite strong semantic understanding and extensive prior knowledge. Conversely, multi-agent reinforcement learning (MARL) can learn environment-aligned policies through interaction, but often suffers from inefficient exploration and heavy reliance on centralized global state information. To achieve complementary advantages, we propose the environment-aligned LLMs (EALLMs). In our framework, an LLM serves as a shared policy for all agents and is updated through online MARL to achieve alignment with the environment. Simultaneously, another LLM, fine-tuned with offline datasets, acts as an information integrator to generate global state for communication purposes. Additionally, we design robust, task-specific prompts tailored to multi-agent systems. Extensive experiments demonstrate that EALLMs outperform classical MARL and LLM-based baselines in both exploration efficiency and overall performance on the SMAC and SMACv2 benchmarks. Ablation studies further confirm EALLMs’ ability to achieve competitive results without relying on explicit global state, while preserving the original capabilities of the LLM during alignment.

Coverage control is a foundational domain within multi-agent systems, which has recently undergone substantial advancements. Model-based and optimization-based control methods have achieved new breakthroughs and applications, while data-driven and learning-based algorithms in coverage control have also yielded significant results. This review examines the evolution and application of coverage control algorithms in multi-agent systems. Moreover, this review then focuses on how contemporary algorithms build on traditional control strategies and integrate cutting-edge data-driven and adaptive learning techniques. Finally, it explores potential future directions, emphasizing the importance of interdisciplinary approaches in overcoming existing challenges and seizing new opportunities in coverage control deployment. This review aims to be a valuable resource for researchers and practitioners, guiding continued exploration in this field.

Structural health monitoring with wireless sensor networks(WSNs) plays an increasingly critical role in modern municipal. While there still remains a gap in getting a comprehensive understanding of complex structural data. Skin diseases can be well diagnosed with modern medical technology, from which similar methods can be learned to improve the intuitiveness and accuracy of structural health monitoring. This work proposes a multi-layered skin-like architecture based method(MSHA), which each layer has its own functions and on the whole presents the disaster situation. This biological structure-inspired architecture has three layers: 1) data substrate layer; 2) connectivity structure layer; 3) pathological manifestation layer, to simulate the three-layer structure of skin. First, a temporal feature extraction method is proposed, which can provide temporal correlations of each node. Second, a dimensional independent spacial feature extraction method is proposed, these first two methods form the connectivity structure layer, which is mainly composed of a spatio-temporal correlation model. Third, a structural health evaluation method for the pathological manifestation layer is proposed to fuse heterogeneous data and calculate multi-granularity structural risk with the features extracted from the connectivity structure layer. The experimental results show that MSHA can achieve not only accurate prediction and intuitive disaster situation, but also low energy consumption and longer lifetime. Note to Practitioners—This paper was motivated by the problem of effectively monitoring the structural health of infrastructure with wireless sensor networks. Existing methods for infrastructure structural health monitoring have space for improvement in maximizing the utilization of correlations between sensor nodes, temporal sequences, and between different sensor types, and fail to produce intuitive results to characterize structural health. In this paper, inspired by the multilayer structure of skin, we propose a new method for analyzing and presenting the structural changes of infrastructure under wireless sensor network data, which fully analyzes the relationship between sensor data in time, space, and type, and is able to predict the structural data in the future period and visually express the current safety situation with size and color information. In this paper, we mathematically describe the model construction method and feature transfer of the constructed intelligent monitoring method. We validate the proposed method using actual tunnel data. The experimental results show that the method is feasible and can accurately indicate the current safety situation in each area while achieving high accuracy in predicting future data, as well as good performance in terms of energy consumption and life cycle. In the future, we will explore optimal strategies for the placement of sensor nodes and improve the convenience and adaptability of the models across multiple scenarios.

This paper considers the distributed cooperative control of physically interconnected multi-agent networks in the presence of nonlinear and unknown uncertainties that is one of the key problems in robot swarms and autonomous vehicle fleet. A distributed adaptive control framework is established, under which edge-based adaptive algorithms and node-based adaptive algorithms are designed, respectively. In the designed protocols, a linear term is used to force the states of all agents to be the same and a nonlinear term inspired by the sliding-mode control is introduced to suppress the effect of the undesirable uncertainties. Compared with the exiting related works, the algorithms presented here are applicable to physically interconnected systems, and they are scalable without requiring any global information and robust to various uncertainties. The results are also extended to the case of non-matching uncertainties. The effectiveness of the protocols is verified by an example of multiple coupled vehicles. demonstrating their potential to enhance the robustness and adaptability of robotic systems in dynamic and complex operational environments.

Although degradation modeling has been widely applied to use multiple sensor signals to monitor the degradation process and predict the remaining useful lifetime (RUL) of operating machinery units, three challenging issues remain. One challenge is that units in engineering cases usually work under multiple operational conditions, causing the distribution of sensor signals to vary over conditions. It remains unexplored to characterize time-varying conditions as a distribution shift problem. The second challenge is that sensor signal fusion and degradation status modeling are separated into two independent steps in most of the existing methods, which ignores the intrinsic correlation between the two parts. The last challenge is how to find an accurate health index (HI) of units using previous knowledge of degradation. To tackle these issues, this article proposes an adaptation-aware interactive learning (AAIL) approach for degradation modeling. First, a condition-invariant HI is developed to handle time-varying operation conditions. Second, an interactive framework based on the fusion and degradation model is constructed, which naturally integrates a supervised learner and an unsupervised learner. To estimate the model parameters of AAIL, we propose an interactive training algorithm that shares learned degradation and fusion information during the model training process. A case study that uses the degradation data set of aircraft engines demonstrates that the proposed AAIL outperforms related benchmark methods.

The key pillar of developing digital city is the ubiquitous sensing of people and the environment. Crowdsensing requires a large number of users to participate in the collection of sensing data, and these data may carry sensitive information, such as identity and location related to the users or sensing object. If this information is eavesdropped, intercepted, and leaked, this may seriously harm the interests of individuals, organizations, and even countries. Therefore, from a privacy perspective, users may be reluctant to open data. While relying on mobile devices used by a large number of ordinary users as the basic sensing unit, it is necessary to include a variety of communication methods to realize the distribution of sensing tasks and to collect the sensing data. Then, to complete the complex crowdsensing tasks, it is important to ensure privacy security in the context of crowdsensing because it is a key problem. In this article, we comb through the development status of crowdsensing in the digital city, emphatically analyze the privacy protection in crowdsensing under the background of digital city, and qualitatively evaluate the existing privacy protection technologies for crowdsensing. Finally, this article presents research challenges and future directions that should be addressed to improve the performance of privacy protection technologies for crowdsensing systems.