Digital twin-driven swarm of autonomous underwater vehicles for marine exploration

Swarm control of autonomous underwater vehicles (AUVs) has been recognized as the foundation for marine exploration. However, the implementation of this task faces two major constraints: excessive communication energy demands and limited environmental perception capabilities. This article proposes a digital twin (DT)-driven swarm control of AUVs solution to overcome these limitations. We first create the digital replicas for each AUV by integrating the dynamics and environmental data. With the collected states from AUVs, a parameter estimator is proposed to predict the flow field, while a swarm networking protocol is designed to reduce the energy consumption. After that, an integral reinforcement learning (IRL)-based swarm controller is proposed to drive the virtual and real AUVs. Based on the interaction information between DT models and AUVs, the virtual-real error optimization algorithm is implemented to minimize the matching errors. Finally, the effectiveness of our solution is verified by the experimental results. These results demonstrate that the DT-driven swarm control of AUVs can improve the underwater situation awareness and prediction accuracy while reducing the communication energy consumption.