GNN-based temporal knowledge reasoning for UAV mission planning systems

Unmanned aerial vehicles(UAVs) are increasingly applied in various mission scenarios for their versatility, scalability and cost-effectiveness. In UAV mission planning systems(UMPSs), an efficient mission planning strategy is essential to meet the requirements of UAV missions. However, rapidly changing environments and unforeseen threats pose challenges to UMPSs, making efficient mission planning difficult. To address these challenges, knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs, missions, and environments. This paper investigates knowledge graph application in UMPSs, exploring its modeling, representation, and storage concepts and methodologies. Subsequently, the construction of a specialized knowledge graph for UMPS is detailed. Furthermore, the paper delves into knowledge reasoning within UMPSs, emphasizing its significance in timely updates in the dynamic environment. A graph neural network(GNN)-based approach is proposed for knowledge reasoning, leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge graph. For relation reasoning, path information is also incorporated to improve the accuracy of inference. To account for the temporal dynamics of the environment in UMPS, the influence of timestamps is captured through the attention mechanism. The effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.