Flocking aims to drive a group of agents connecting with each other to interact in a complex way, thereby emerging the behavior of group aggregation. Owing to the prevalence and complexity of cooperation and competition relationships among agents, it is practical and challenging to explore the realization of flocking behavior on multiagent coopetition networks. With that in mind, this article focuses on the study of flocking behavior for Cucker–Smale multiagent model over coopetition networks with communication delays. In this model, the cooperation/competition degree between agents is portrayed as a weight function with respect to communication distance, e.g., the closer (farther) the communication distance, the stronger (weaker) the cooperation/competition degree. With the help of analysis tools consisting of edge composites and substochastic matrices, the algebraic relationships between cooperation and competition degrees are established to ensure the emergence of flocking behavior. Moreover, mathematical expressions are given to show the effect of different communication delays on the agents’ final aggregation upper bound and convergence rate. In the end, the theoretical results are validated through computer simulations.

This contribution explores the flocking dynamic behavior of multi-agent networks with limited communication resources, where there are both cooperative and antagonistic relationships among agents. Taking into account the shortage of communication resources, a distributed control protocol with edge-asynchronous communications is designed, in which only part of the communication links are awakened at each time instant to perform information transmission. A product approach of super-stochastic matrices is utilized to rigorously check the convergence of the dynamic model. With this method, algebraic conditions on cooperative and antagonistic relationships among agents are established so that the entire multi-agent network emerges with flocking dynamics. At last, theoretical results are tested and verified through computer simulations.

Localization is a key technology to ensure the effective operation of wireless sensor networks in different environments. Due to the prevalence of cyber-attacks in real-world application scenarios, ensuring the accuracy of localization under denial-of-service (DoS) attacks is a growing concern. Existing research focuses on distributed localization of static sensor networks under DoS attacks. This article aims to extend the study of distributed localization in mobile sensor networks subject to DoS attacks. Under DoS attacks, communication between sensor nodes can become intermittent, resulting in the time-varying characteristic for communication networks among all sensor nodes, which poses a challenge for successful localization. To overcome this challenge, this article proposes a distributed iterative localization algorithm using relative barycentric coordinates and distance measurements. Based on a hybrid approach composed of graph composition and sub-stochastic matrix, a comprehensive analysis of the convergence, rate and complexity of the localization algorithm is presented. At last, the theoretical results are verified by experimental examples.

This paper considers the dynamic coverage control of multi-rotor Unmanned Aerial Vehicles (UAVs) with the limited sensory range, which aims to collect sensor information from all points of interest in the given task area until the desired prescribed level is reached. However, the unknown environments are usually unavoidable for coverage task, where the presence of various obstacles and communication interferences affect the flight safety and communication stability of UAVs. Therefore, collision avoidance and connectivity maintenance are considered as the two safety issues in this paper, in which connectivity maintenance ensures the communication environment for UAVs to collaboratively accomplish task, and collision avoidance is used for UAVs to avoid obstacles and neighbors. In order to realize dynamic coverage control with safety constraints based on local environment information, this paper proposes the reinforcement learning-based algorithm with shield, where the shield designed by discrete-time Control Barrier Function (CBF) not only ensures the safety of the UAVs in the learning and control phases, but also maximizes the coverage performance of UAVs. In addition, each UAV only relies on local information to generate safe actions for advancing the coverage process during the execution phase. Finally, the effectiveness of the algorithm is verified by numerical simulations and physical experiment. Note to Practitioners —A typical application scenario of dynamic coverage control is search and rescue (SAR), in which UAVs equipped with multiple sensors focus on monitoring areas where trapped people may be present, e.g., anomalous areas detected by infrared sensors due to human body temperature. Since SAR always occurs in unknown environments, it is crucial to ensure the safety of UAVs during missions, of which the safety issues considered in this paper include collision avoidance and connectivity maintenance. To perform the SAR mission safely, we construct the CBF-based shield, which minimizes corrections the exploration actions of the UAVs and ensures the safety of the cluster during the mission. In addition, UAVs are difficult to obtain global environmental information in unknown environments and only rely on their sensors to collect local information. Therefore, the reinforcement learning algorithm with shield proposed in this paper adopts the centralized training and decentralized execution strategy, where the UAVs only need local observation information to plan their next actions. Physical experiments were also conducted to validate the feasibility of implementing the proposed algorithm using real UAVs.

With the advantages of large transmission capacity, high data rate and good confidentiality, inter-satellite laser communication has become a promising alternative for satellite networks. However, with more and more satellite nodes being launched into the space, the number of space debris will increase rapidly. Due to the nature of linear transmission of laser links, space debris may cause the uncertain interruption of laser links, and further degrade the link survivability of satellite optical networks. In this paper, we design a risk perception model based on machine learning for inter-satellite laser links risk caused by space debris. Based on this model, we propose an adaptive snapshot routing strategy based on flexible granularity (ASRS-FG), which can effectively avoid the risk of inter satellite link interruption caused by space debris. Finally, to verify the performance of the algorithm, we built a satellite optical networks simulation platform. The simulation results show that our model's prediction accuracy on space debris risk reaches 95%. Compared with the equal-length snapshot routing strategy (ESRS), ASRS-FG effectively avoids the use of risky laser links and achieves higher service success ratio and fewer snapshot switching times.