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Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations. [ABSTRACT FROM AUTHOR]

A partial space elevator (PSE) is a multi-body tethered space system in which the main satellite, typically an ultra-large spacecraft or a space station in a higher orbit, is connected to a transport spacecraft in a lower orbit via a tether, maintaining orbital synchronization. One or more climbers can move along the tether driven by electric power, enabling cross-orbital payload transportation between the two spacecraft. The climbers' motion significantly alters the main satellite's orbital states, compromising its safe and stable operation. The dynamic coupling and nonlinearity of the PSE further exacerbate this challenge. This study aims to preliminarily address this issue by proposing a new mission planning strategy. This strategy utilizes reinforcement learning (RL) to select the waiting interval between two transfer missions, thereby maintaining the main satellite's orbital motion in a stable state. Simulation results confirm the feasibility and effectiveness of the proposed mission-based method. [ABSTRACT FROM AUTHOR]

The scale factor serves as a ruler for converting raw phase measurements into physical displacements and significantly impacts the preprocessing of data from the Laser Ranging Interferometer (LRI) in laser ranging systems. In the current GRACE Follow-On (GRACE-FO) mission for low–low tracking gravity satellites, most of the existing LRI scale factor estimation algorithms heavily rely on cross-calibration between instantaneous/biased ranges from the Ka-Band Ranging Interferometer (KBR) and the LRI. However, due to the nonlinearity of the objective function (which includes terms involving the product of scale and time shifts), the scale factor estimation may absorb errors from timing noise. Moreover, future gravity missions or gravity detection tasks may no longer incorporate KBR ranging instruments. To address these challenges, this paper proposes an energy-based method for scale factor estimation using inter-satellite baseline solutions. Comparative analysis indicates that the proposed method effectively disentangles two parameters in the objective function and can be applied in scenarios where KBR data are unavailable, demonstrating promising prospects for practical application. The experimental results show that when the KBR validation residuals are lower than 0.8 mm, the SYSU LRI1B V01 products exhibit residuals below the payload design accuracy thresholds in the frequency band of 2 mHz to 0.1 Hz. Additionally, the stability of the scale factors obtained from the baseline can reach 10−7. Although this is still below the required precision of better than 10−8 for the laser frequency stability in next-generation gravity satellites, advancements in orbit determination technology and the enhanced stability of GPS receivers offer potential for future precision improvements. Currently, this method appears suitable for roughly extracting the scale factor as a stochastic mean over several months or serving as a backup validation strategy for future missions, but it is not well suited to measure day-to-day variations. [ABSTRACT FROM AUTHOR]

As a new organic photocatalyst, polymeric carbon nitride (CN) has shown good application potential in the field of photoelectrochemistry due to its unique physical and chemical properties, but its application has been seriously hindered due to its inherent characteristics such as the difficulty in charge separation. In this study, FeOOH modified CN photoanode (CN−Fe) was constructed to investigate the effect of the cocatalyst on the charge injection capacity of organic semiconductor photoelectrodes. The experimental results demonstrate significant improvement in the charge injection efficiency of the photoanode due to the introduction of FeOOH cocatalyst, leading to enhanced photoelectrochemical performance with approximately 2.4 times increase in photocurrent density. By thoroughly investigating the mechanism behind the loading of FeOOH on the polymeric carbon nitride photoanode, we gained profound insights into the behavior of charge carriers and reaction kinetics during the photoelectrocatalytic process. [ABSTRACT FROM AUTHOR]

This article addresses the attitude manoeuvre control without unwinding based on variable gain (i.e. adaptive) super-twisting algorithm (STA) and the modified Rodrigues parameters (MRPs). First, a selection logic for the MRP set and its shadow set is designed in order to construct an attitude control system model for rigid spacecraft. Second, a nonlinear sliding function is presented, and on the sliding phase, finite-time convergence and anti-unwinding performance are guaranteed. As a result, a first-order sliding mode controller is created to ensure finite-time convergence and anti-unwinding performance on the reaching phase. To alleviate the chattering and improve convergence speed, an adaptive STA is presented, and the finite-time convergence and anti-unwinding performance are guaranteed on the reaching phase. Furthermore, the adaptive gains are designed based on dual-layer adaptive law, which allows the gains to be increased and decreased as appropriate. It is further demonstrated that when the controller gains are time-varying according to adaptive law, the convergence property and anti-unwinding performance are guaranteed. Numerical simulations are carried out to illustrate the closed-loop system's performance under the proposed control scheme. [ABSTRACT FROM AUTHOR]

The problem of UAVs’ formation control in the process of motion is investigated in this paper. A formation control method based on artificial potential field of UAVs is proposed, established on the collision avoidance, aggregation and speed matching rules of UAVs. First establish the UAVs’ kinetic model in accordance to the motion rules, then design the formation control algorithm based on artificial potential field function, which is used to control the formation during the movement of UAVs. Finally, the results of simulation experiment show that the proposed formation control method in this paper is effective and has the advantages of easy realization, good real-time performance and excellent robustness.