Digital twin network (DTN) technology provides significant support for intelligent applications of unmanned aerial vehicle (UAV) swarms. However, related research focuses on DTN applications and lacks attention to the construction and maintenance of high-fidelity digital twin (DT) models. In this paper, we first develop a DT simulation platform for UAV swarms. Then, a dynamic data-driven DT model calibration scheme is proposed on the example of the most fundamental flight state of a UAV. The scheme utilizes parameter identification to estimate offline the key parameters of the measurement model and system deviations. Furthermore, the flight state is corrected online utilizing data assimilation actuated on the actual and virtual data. Simulation experiments on the DT simulation platform are conducted, and the effects of data sampling rate on calibration accuracy and computational load are analyzed. The results demonstrate that parameter identification and data assimilation significantly improve the fidelity of the DT model in terms of the optimal sub-pattern assignment (OSPA) metric to different degrees.

As an advanced framework, the digital twin network (DTN) provides effective management and decision support for autonomous aerial vehicle (AAV) swarms and has become a recent research hotspot. The effectiveness of many DTN applications relies on the assumption that high-fidelity digital twin (DT) models exist and are readily available. However, constructing such high-fidelity DT models of AAV swarms is a challenging task, especially in complex and dynamic environments. Despite its importance, there is a notable lack of research focused on the construction of high-fidelity DT models specifically for AAV swarms. This study proposes a dynamic data-driven approach for constructing and calibrating DT models of AAV swarms to achieve long-term consistency with real-world AAV behaviors. The method leverages parameter identification to estimate key parameters of DT models and data assimilation to refine and calibrate the model. It can provide high-fidelity DT AAV models for artificial intelligence model training and facilitate AAV swarm DTN from concept to real application. Additionally, this article developed a DT simulation platform for AAV swarms, validating the proposed method through software-in-the-loop simulations and physical testing. Results indicate that the optimal subpattern assignment metric decreases by an average of 79.2% after calibration, significantly improving the DT model’s fidelity.

Cooperative target search (CTS) technology is highly desirable in various multi-autonomous aerial vehicle (AAV) applications. However, searching for unknown targets in a dynamic threatening environment is a challenging problem, especially for AAVs with limited sensing range and communication capabilities. Besides, traditional searching methods lack scalability and efficient collaboration among the AAV swarm in dynamic environments. In this work, a digital twin (DT)-enabled distributed CTS approach was presented for AAV swarms and achieving sim-to-real transfer. Specifically, a new scalable multi-agent reinforcement learning (MARL) based algorithm called SAMARL is adopted to improve effectiveness and adaptability, combining a multi-head attention mechanism. In SAMARL, a scalable observation space with graph representation and an environmental cognition map is designed to thoroughly consider the target search rate, area coverage, and safety assurance. Then, a DT-driven training framework is proposed to facilitate the continuous evolution of MARL models and address the tradeoff between training speed and environment fidelity. Furthermore, we innovatively develop a distributed AAV swarm digital twin cooperative target search validation system, including real flight control, communication simulation tools, and a 3D physics engine. Extensive simulations validate its superiority compared to state-of-the-art strategies. More importantly, we also conduct real-world flight experiments on different scale mission areas and AAV swarms, further demonstrating the generalization and scalability of trained models.

Over the past decades, unmanned aerial vehicles (UAVs) have been widely used in both military and civilian fields. In these applications, flocking motion is a fundamental but crucial operation of multi-UAV systems. Traditional flocking motion methods usually designed for a specific environment. However, the real environment is mostly unknown and stochastic, which greatly reduces the practicality of these methods. In this article, deep reinforcement learning (DRL) is used to realize the flocking motion of multi-UAV systems. Considering that the sim-to-real problem restricts the application of DRL to the flocking motion scenario, a digital twin (DT)-enabled DRL training framework is proposed to solve this problem. The DRL model can learn from DT and be quickly deployed on the real-world UAV with the help of DT. Under this training framework, this article proposes an actor–critic DRL algorithm, named behavior-coupling deep deterministic policy gradient (BCDDPG), for the flocking motion problem, which is inspired by the flocking behavior of animals. Extensive simulations are conducted to evaluate the performance of BCDDPG. Simulation results show that BCDDPG achieves a higher average reward and performs better in terms of arrival rate and collision rate compared with the existing methods.