Accurately predicting aortic valve movement under coronary influence is critical for personalized cardiac interventions and virtual surgery planning. Conventional Fluid-Structure Interaction (FSI) models tend to neglect modeling coronary arteries due to their complexities, leading to bias in leaflet motion simulations. To overcome this limitation, we propose a combination of spatiotemporal graph convolution and Transformer based gated (Gated-STGFormer) deep learning framework, which learns to reconstruct coronary-modulated leaflet motion from simulations without explicit coronary arteries. The framework integrates Graph Convolutional Networks (GCNs) for spatial dependency modeling and Transformer for temporal dependency, with both encoding and gating mechanisms to effectively capture spatiotemporal couplings. Quantitative evaluation demonstrated that it reproduces the spatiotemporal movements of leaflets under the coronary arteries with a high degree of fidelity. By addressing anatomical simplifications in conventional simulations, this method provides a physics-informed and computationally efficient surrogate model with strong clinical applicability. Our findings suggest that the Gated-STGFormer effectively incorporates spatiotemporal modal information, and can serve as a module for coronary artery compensation in a preoperative planning system, enabling more realistic and personalized valve biomechanical simulations.

The multitarget tracking of multirobot systems has a wide range of applications, including urban security, search, and rescue. However, these tasks often take place in obstacle-rich environments, posing significant challenges, such as an unknown global map, unclear target locations, and inefficient resource allocation. In this article, we propose an air–ground cooperative hierarchical method for multitarget tracking. The uncrewed aerial vehicle (UAV) is equipped with a fisheye camera to provide a wide field of view (FOV) for multitarget detection and an initial global map generated from segmented aerial images. Multiple targets are assigned to uncrewed ground vehicles (UGVs) using the task allocation method, which is based on the PIO and auction mechanism. UGV track targets through motion planning to avoid obstacles and keep their assigned targets in the FOV. Experimental validation confirmed the effectiveness of our method, demonstrating that UGVs can autonomously and reliably track targets in obstacle-rich environments with UAV assistance.

Dragonflies show impressive flying skills by achieving both high efficiency and agility. They can perform distinctive flight maneuvers, such as flying backwards, which has proven to be achieved through "force vectoring" mechanism recently. In this paper, to explore the agile flight ability of dragonflies on man-made flapping wing systems, we designed, optimized and fabricated a dragonfly-inspired flapping wing robot (DFWR) with inclinable stroke plane control degrees. The proposed platform employs a four-wing configuration, each of which integrates an extra servo motor to enable the rotation of the flapping plane and imitate the "force vectoring" mechanism. Besides, referring to the flapping kinematics of dragonflies, the installation angle and wing pitch angle of the proposed DFWR are optimized considering the total lift and energy consumption through multiobjective optimization based on NSGA-II method. The "force vector" produced by the proposed platform has been illustrated through both theoretical method and experimental method. Moreover, the feasibility of the design is further verified through a series of operation validation experiments. Such a robot has the potential to provide a highly biomimetic platform to validate the flight mechanism studying of Odonata as well as the relative on-board applications such as bio-inspired vision.