Over the past decades, Unmanned Air Vehicles (UAVs) have achieved outstanding performance in military, commercial and civilian applications. UAVs are increasingly appearing in the form of swarms or formations to meet higher mission requirements. Communication plays an important role in UAV swarm control and coordination. The communication architecture defines how information is exchanged between UAVs or between UAVs and the central control center. Routing protocols help provide reliable end-to-end data transmission. Therefore, it is particularly important to design UAV swarm communication architectures and routing protocols with high performance and stability. This review article details four communication architectures including the advantages and disadvantages. Applicable scenarios are also discussed. In addition, a systematic overview and feasibility research of routing protocols are presented in this paper. To spur further research, the open research issues of UAV swarm communication architectures and routing protocols are also investigated.

Over the past decade, unmanned aerial vehicles (UAVs) have demonstrated increasing attention and promise. They demonstrate great potential for application in both civilian and military fields. In this context, collision-avoidance (CA) approaches play an extremely important role in collaborative and noncollaborative UAVs in a common airspace for reliable and safe operation. In this article, we give an analysis and description of the CA functions, including the CA process, state sensing, conflict detection, and conflict resolution. Then, an overview of the recent progress in CA technologies is systematically provided, particularly in terms of sensing techniques, conflict detection, and conflict resolution. The state-sensing methods include ground-based and air-based sensing technologies, and cooperative and non-cooperative sensing. The conflict-detection methods contain conflict detection with certainty and without certainty. The conflict-resolution methods include methods for individual UAVs and for UAV formation. In addition, several challenging problems are introduced to develop a lucid research direction. This review gives a unique perspective and contributes toward the further development of CA approaches for UAVs.

Due to their strong risk tolerance, low manufacturing cost and good maneuverability, unmanned aerial vehicles (UAVs) have been widely used in various fields. Among related challenges, coordinated task assignment is a key scientific issue for autonomous control of UAVs. In this paper, based on the idea of fuzzy C-means (FCM) clustering and the ant colony optimization (ACO) algorithm, a cooperative multiple task reallocation problem with target precedence constraints for heterogeneous UAVs is proposed. The contributions of this research are the performance evaluation of the original algorithms in a dynamic context, consideration of changes in some attributes of the environment, and the extension of these algorithms to properly address more realistic dynamic emergent adjustment scenarios. According to the corresponding task reallocation strategy, the scenarios are divided into three categories: the complete redistribution strategy can effectively cope with scenarios where tasks have changed significantly, the partial adjustment strategy can induce partial responses to the changes of individual tasks, and group redistribution can effectively solve the problem of task target threat rating changes. The simulation results show that the dynamic reallocation model of multi-UAV tasks in dynamic emergent adjustment scenarios can achieve better performance to complete the corresponding tasks based on the proposed scheme. In addition, we deployed the developed graphical modeling and analysis software (GMAS) platform to implement the dynamic reallocation model of multi-UAV tasks in dynamic emergent scenarios, and the validity and reliability of the proposed task reallocation model were verified.

Effectively improving the optimization performance of artificial electric field algorithm (AEFA) and broadening its application domain can aid in providing robot path planning in three-dimensional (3D) complex scenes. This paper effectively proposes an improved AEFA (I-AEFA) and creatively applies it to robot path planning. The algorithm introduces three mechanisms to enhance the exploration ability and convergence accuracy of the population: parameter adaptation, reverse learning, and Cauchy mutation. Next, the benchmark terrain model accurately models the 3D environment, and the global path planning problem is solved using a combination of I-AEFA and cubic spline interpolation. Then, a large number of virtual simulation experiments are conducted to evaluate the algorithm's three improved mechanisms, various control point counts, as well as single and multi-robot configurations before migrating the algorithm to the graphical modeling and analysis software (GMAS) for hardware-in-the-loop simulation experiments. Finally, the experimental results are analyzed qualitatively and quantitatively using a variety of visualization techniques and two nonparametric test methods, demonstrating that the I-AEFA proposed in this paper has good optimization performance and is highly effective, reliable, and scalable for solving robot path planning problems.

The use of artificial intelligence (AI) has increased since the middle of the 20th century, as evidenced by its applications to a wide range of engineering and science problems. Air traffic management (ATM) is becoming increasingly automated and autonomous, making it lucrative for AI applications. This paper presents a systematic review of studies that employ AI techniques for improving ATM capability. A brief account of the history, structure, and advantages of these methods is provided, followed by the description of their applications to several representative ATM tasks, such as air traffic services (ATS), airspace management (AM), air traffic flow management (ATFM), and flight operations (FO). The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages: (i) these methods provide alternative approaches to conventional physical modeling techniques, (ii) these methods do not require knowing relevant internal system parameters, (iii) these methods are computationally more efficient, and (iv) these methods offer compact solutions to multivariable problems. In addition, this review offers a fresh outlook on future research. One is providing a clear rationale for the model type and structure selection for a given ATM mission. Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission. These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.