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The attitude, orbit, and shape control actuators of large space structures on orbits are functional overlapping and task coupled, resulting in actuator redundancy and high-energy consumption. This paper explores the integrated attitude–orbit–shape control problem of large space ring structures and proposes an online task switching and scheduling method for multiple actuators of space structures (OTSS-MASS). An adaptive allocation framework is proposed for the task switching of distributed actuators. The objectives of attitude–orbit–shape control are decomposed into attitude adjustment, orbit change, and shape maintenance tasks through a task decomposition algorithm. Task value estimation models are designed to guide a greedy task allocation of actuators. The adaptive allocation framework achieves online task reconfiguration for multiple distributed actuators, thereby enhancing the maneuverability of large space structures, improving actuator work efficiency, and reducing energy consumption in orbit. Simulation results demonstrate that OTSS-MASS outperforms the conventional actuator distribution methods. The overall effectiveness is improved by over 24% in terms of the comprehensive index, time index, trajectory index, and shape index.

In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To address challenging environments, especially unstructured ones, IMU predictions are used to compensate for pose estimation in the visual and LiDAR components. Specifically, the accuracy of IMU predictions is enhanced by increasing the correction frequency of IMU bias through data integration from the LiDAR and visual modules. To reduce the impact of random errors and measurement noise in LiDAR points on visual depth measurement, cross-validation of visual feature depth is performed using reprojection error to eliminate outliers. Additionally, a structure monitor is introduced to switch operation modes in hybrid point cloud registration, ensuring accurate state estimation in both structured and unstructured environments. In unstructured scenes, a geometric primitive capable of representing irregular planes is employed for point-to-surface registration, along with a novel pose-solving method to estimate the UAV’s pose. Both private and public datasets collected by UAVs validate the proposed system, proving that it outperforms state-of-the-art algorithms by at least 12.6%.

Summary: The tumor microenvironment (TME) is restricted in metabolic nutrients including the semi-essential amino acid arginine. While complete arginine deprivation causes T cell dysfunction, it remains unclear how arginine levels fluctuate in the TME to shape T cell fates. Here, we find that the 20-μM low arginine condition, representing the levels found in the plasma of patients with cancers, confers Treg-like immunosuppressive capacities upon activated T cells. In vivo mouse tumor models and human single-cell RNA-sequencing datasets reveal positive correlations between low arginine condition and intratumoral Treg accumulation. Mechanistically, low arginine-activated T cells engage in metabolic and transcriptional reprogramming, using the ATF4-SLC7A11-GSH axis, to preserve their suppressive function. These findings improve our understanding of the role of arginine in human T cell biology with potential applications for immunotherapy strategies.

This study uses a Bezier curve-based shaping (BCBS) approach based on the dynamic model to quickly generate 3D cooperative trajectories for unmanned aerial vehicles (UAVs). It can facilitate the solution of the flight trajectory problem that occurs after the reallocation of mission points. Trajectory coordinates are expanded based on the Bezier curves that naturally satisfy boundary conditions (BCs); trajectories with the minimum flight time and those satisfying the dynamic constraints and collision-free conditions can be obtained by adjusting the Bezier coefficients. A three-UAV flight simulation is carried out to verify the effectiveness of the proposed method. Further, the performance of the proposed method is compared with that of the Gauss pseudospectral method (GPM). The simulation results of the BCBS method are used as the initial values for GPM optimization. It is demonstrated that the BCBS method requires a lower computation time than the direct solver, which is only 0.07% of the latter, and obtains similar optimization results (3.34% difference). This is considerably important for the rapid generation of optimized trajectories with the limited computing power of onboard computers. Furthermore, this method is expected to achieve online collaborative trajectory generation for multiple UAVs in view of its high computational efficiency.

To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.