Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

Due to the high maneuverability and strong adaptability, autonomous unmanned aerial vehicles (UAVs) are of high interest to many civilian and military organizations around the world. Automatic path planning which autonomously finds a good enough path that covers the whole area of interest, is an essential aspect of UAV autonomy. In this study, we focus on the automatic path planning of heterogeneous UAVs with different flight and scan capabilities, and try to present an efficient algorithm to produce appropriate paths for UAVs. First, models of heterogeneous UAVs are built, and the automatic path planning is abstracted as a multi-constraint optimization problem and solved by a linear programming formulation. Then, inspired by the density-based clustering analysis and symbiotic interaction behaviours of organisms, an adaptive clustering-based algorithm with a symbiotic organisms search-based optimization strategy is proposed to efficiently settle the path planning problem and generate feasible paths for heterogeneous UAVs with a view to minimizing the time consumption of the search tasks. Experiments on randomly generated regions are conducted to evaluate the performance of the proposed approach in terms of task completion time, execution time and deviation ratio.

Due to the strong adaptability and high flexibility, unmanned aerial vehicles (UAVs) have been extensively studied and widely applied in both civil and military applications. Although UAVs can achieve significant cost reduction and performance enhancement in large-scale systems by taking full advantage of their cooperation and coordination, they result in a serious cooperative behaviour control problem. Especially in dynamic environments, the cooperative behaviour control problem which has to quickly produce a safe and effective behaviour decision for each UAV to achieve group missions, is NP-hard and difficult to settle. In this work, we design a global-and-local attention-based reinforcement learning algorithm for the cooperative behaviour control problem of UAVs. First, with the motion and coordination models, we analyze the collision avoidance, motion state update, and task execution constraints of multiple UAVs, and abstract the cooperative behaviour control problem as a multi-constraint decision-making one. Then, inspired from the human-learning process where more attention is devoted to the important parts of data, we design a multi-agent reinforcement learning algorithm with a global-and-local attention mechanism to cooperatively control the behaviours of UAVs and achieve the coordination. Simulation experiments in a multi-agent particle environment provided by OpenAI are conducted to verify the effectiveness and efficiency of the proposed approach. Compared with baselines, our approach shows significant advantages in mean reward, training time, and coordination effect.

Converting natural language text into executable SQL queries significantly impacts the healthcare domain, specifically when applied to electronic medical records. Given that electronic medical records store extensive patient information in a relational multitable database, developing a Text-to-SQL parser would enable the correlation of intricate medical terminology through semantic parsing. A major challenge is designing a versatile Text2SQL parser applicable to new databases. A critical step towards this goal involves schema linking - accurately identifying references to previously unseen columns or tables during SQL creation. In response to these key challenges, we propose a novel framework—Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records (LI-EMRSQL). This model leverages the Poincaré distance metric detection procedure, utilizing induced relations to enhance the performance of pre-existing graph-based parsers and improve schema linkage. To enhance the generalizability of LI-EMRSQL, the detection process is completely unsupervised and does not necessitate additional parameters. On two conventional Text2SQL datasets and two EMRs Text2SQL datasets, the system delivers SOTA performance. Furthermore, notable enhancements in the model's comprehension and alignment of schemas are observed.