Hierarchical-Learning-Based Task Assignment for Heterogeneous Multi-AUV-UG Collaborative System to Collect Data From Underwater Sensors

In this study, the task assignment problem for heterogeneous underwater vehicle collaborative system, which involves autonomous underwater vehicles (AUVs) and underwater gliders (UGs), is studied for high-efficiency data collection. UGs and AUVs show different motion modes. The advantages of different motion modes can be mutually complemented to achieve the preference-matched task assignment results, which show greatly promising prospect to enhance the data collection efficiency. Most of existing underwater task assignment algorithms focus on the single-type vehicles, which can not be applied to the heterogeneous system. To address this issue, a hierarchical learning algorithm is proposed. Firstly, based on the evaluated emergency degree of tasks, the preliminary-task-assignment hierarchy is proposed to assign the emergency tasks to AUVs and assign the non-emergency tasks to UGs, thereby achieving the preference-matched task assignment. Therefore, the collaborative efficiency of heterogeneous system can be enhanced. Then, in the UG-task-assignment hierarchy, the adaptive serial cluster mechanism is proposed to extract the high utility task-connectivity regions for UGs, thereby fully leveraging the UG advantages in region data collection. Furthermore, in the AUV-task-assignment hierarchy, the extended self-organizing mapping neural network is constructed to eliminate the disorganization of neuronal loops. As a result, the crossed paths of AUVs can be excluded to reduce the energy consumption. Finally, the superior performance is verified by numerical results.