The search for axions sits at the intersection of solving critical problems in fundamental physics, including the strong C P problem in QCD, uncovering the nature of dark matter, and understanding the origin of the Universe’s matter-antimatter asymmetry. The measurement of axion mediated spin-dependent interactions offers a powerful approach for axion detection. However, it has long been restricted to regions outside the “axion window” due to a significant trade-off: the need to effectively suppress the magnetic leakage from highly polarized spin sources while simultaneously detecting subfemtotesla level exotic physics signals at sub-decimeter-scale distances. In this work, we report new experimental results on axion mediated exotic spin-spin interactions using an iron-shielded SmCo 5 spin source in combination with a specially designed self-compensation comagnetometer. Employing a composite shielding structure, we achieved a suppression of the magnetic field by up to 10 11 . This enabled us to establish new constraints on the coupling between electrons and neutrons, improving previous experimental limits by up to 10 000 times within the axion window. Furthermore, we also set the strongest constraints on the coupling between electrons and protons. The proposed method holds substantial potential not only for advancing the search for new physics beyond the standard model but also for enabling transformative applications in biological and chemical research.

Unmanned aerial vehicle (UAV) light shows (UAV-LS) have a wow factor due to their advantages in terms of environment friendliness and controllability compared to traditional fireworks. In this paper, a UAV-LS system is developed including a collision-free formation transformation trajectory planning algorithm, a software package that facilitates animation design and real-time monitoring and control, and hardware design and realization. In particular, a dynamic task assignment algorithm based on graph theory is proposed to reduce the impact of UAV collision avoidance on task assignment and the frequency of task assignment in the formation transformation. In addition, the software package consists of an animation interface for formation drawing and 3D animation simulation, which helps the monitoring and control of UAVs through a real-time monitoring application. The developed UAV-LS system hardware consists of subsystems of decision-making, real-time kinematic (RTK) global positioning system (GPS), wireless communication, and UAV platforms. Outdoor experiments using six quadrotors are performed and details of implementations of high-accuracy positioning, communication, and computation are presented. Results show that the developed UAV-LS system can successfully complete a light show and the proposed task assignment algorithm performs better than traditional static ones.

Space situational awareness (SSA) plays an important role in maintaining space advantages. Task planning is one of the key technologies in SSA to allocate multiple tasks to multiple satellites, so that a satellite may be allocated to supervise multiple space objects, and a space object may be supervised by multiple satellites. This paper proposes a hierarchical and distributed task-planning framework for SSA systems with focus on fast and effective task planning customized for SSA. In the framework, a global task-planner layer performs satellite and object clustering, so that satellites are clustered into multiple unique clusters on the basis of their positions, while objects are clustered into multiple possibly intersecting clusters, hence allowing for a single object to be supervised by multiple satellites. In each satellite cluster, a local task planner performs distributed task planning using the contract-net protocol (CNP) on the basis of the position and velocity of satellites and objects. In addition, a customized discrete particle swarm optimization (DPSO) algorithm was developed to search for the optimal task-planning result in the CNP. Simulation results showed that the proposed framework can effectively achieve task planning among multiple satellites and space objects. The efficiency and scalability of the proposed framework are demonstrated through static and dynamic orbital simulations.

Air-ground coordination systems are usually composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV). In such a system, UAVs can utilize their much more perceptive information to plan the path for UGVs. However, the correctness and accuracy of the planned route are often not guaranteed, and the communication and computation burdens increase with more sophisticated algorithms. This paper proposes a new type of air-ground coordination framework to enable UAVs intervention into UGVs tasks. An event-triggered mechanism in the null space behavior control (NSBC) framework is proposed to decide if an intervention is necessary and the timing of the intervention. Then, the problem of whether to accept the intervention is formulated as an integer programming problem and is solved using model predictive control (MPC). Simulation results show that the UAV can intervene in UGVs accurately and on time, and the UGVs can effectively decide whether to accept the intervention to get rid of troubles, thereby improving the intelligence of the air-ground coordination system.