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The Yutu 2 lunar rover has been engaged in scientific research on the lunar surface for more than three years. In order to maximize the efficiency of planning and driving for the Yutu 2, the distance between the beginning and end stations of each movement is large. This causes significant scale differences and complex affine transformations between the images in adjacent stations, which usually cause failure of image matching and navigation. To address the above problems, current methods use the single-dimensional pixel spatial resolution model based on imaging geometry to analyze the reasons for failed or successful image matching. However, they are not adaptable to images with complex affine transformations. In this article, a general 2-D pixel spatial resolution model based on the algebraic derivation of the homography transformation is proposed. With the proposed model, the scale difference between images of adjacent stations can be quantitatively analyzed, and the rate of successful matching between images that have complex affine transformation and scale differences is increased. This will benefit engineering applications, such as visual localization and navigation. Experiments under various conditions, such as the horizontal and inclined lunar surface, are performed to verify the effectiveness of the proposed method.

Tracking-and-inserting a moving peg using a robot manipulator is a challenging task in manufacturing. In the past decades, various visual-based methods have been proposed for robotic manipulating static targets, which usually ignore the time delay in robot command transmission and image processing. However, for tracking and inserting a moving peg, time delays cannot be overlooked because they can reduce the tracking performance and even cause manipulations to fail. In this article, a robot visual-based control with a time-delay compensator is presented to solve the problem of inserting a moving peg. The time-delay compensator was designed using radial basis function neural network, a feedback compensator aimed at eliminating the tracking errors caused by the time delays. Thus, we could manipulate a moving object using a commercial industry robot, even with the time-variant delays in the control loop. Furthermore, the visual-based controller with the pseudoinverse image Jacobian matrix was designed using a linearization model. Thus, the matrix could be efficiently updated using the model. In the experiment, we inserted a peg into a moving hole using an eye-in-hand robot with precision.

Surface 3-D mapping of small bodies is crucial for planning in situ deep-space explorations, yet current approaches rely heavily on manual processing and significantly increase time costs. To achieve autonomous and efficient 3-D mapping of small bodies, we propose a visual-based autonomous 3-D mapping framework that integrates neural radiance fields (NeRFs) with deep reinforcement learning (DRL). We introduce a NeRF variant, which is built within the optimization loop of DRL, for instant 3-D mapping and quantitative uncertainty estimation of the global mapping. We employ the uncertainty estimation to optimize the DRL policy, thereby predicting the next best state that minimizes uncertainty in surface mapping. Moreover, we introduce a new six degrees of freedom (6 DoF) simulation environment that simulates the visual-based 3-D mapping process of small bodies, and demonstrate that our framework significantly enhance 3-D mapping quality. Our framework provides a new technological direction for small body exploration missions, potentially enhancing efficiency while also advancing operational reliability.

Efficient visual localization of lunar rovers is essential for long-range autonomous exploration missions and the construction of the International Lunar Research Station. Given the communication delay and bandwidth limitations between the Earth and the Moon, we propose a novel cross-view localization framework that autonomously registers a single rover image to an orbital image. We developed a bird’s-eye-view (BEV) feature synthesis method that integrates geometric projection, cross-scale feature transfer (CSFT), and contour guidance mechanism (CGM). The basic principle involves first projecting rover features into the BEV perspective through geometric projection, then reconstructing BEV features in reference to orbital features using CSFT. This process is guided by CGM, enhancing the expression of rich terrain contours in BEV and orbital features, thereby improving the viewpoint and scale consistency of cross-view features. By conducting dense spatial correlation searches between BEV and orbital features, we can accurately estimate the position of the lunar rover. Additionally, we introduce a lunar surface simulation environment and construct the lunar cross-view localization (LCVL) simulation dataset based on this environment to demonstrate the framework’s effectiveness. Our research offers a new solution for rover localization, potentially improving the efficiency of future exploration missions.

Reliable transient thermal analysis plays a very important role in the engine safety analysis. Transient conjugate heat transfer simulation is an important way of temperature analysis. But there exists a great disparity in the time scales between solid conduction and fluid convection. The calculation cost of transient conjugate heat transfer analysis is very huge because of the tiny time step of computational fluid dynamics. The Bi-Fo time scaling method is proposed to improve the computational efficiency of transient conjugate heat transfer. On the one hand, this method carries out a similar transformation on solid heat conduction, scaling the calculation time with the product of density and specific heat capacity to maintain the consistency of Fourier number. On the other hand, it takes very short time for the fluid domain to recover stability after a boundary disturbance. Based on the above characteristic, the flow time is directly compressed to the same as that of the solid domain. It is verified by Mark Ⅱ vane that increasing the solid thermal diffusivity can reduce the time scale of heat conduction. In the situation of rapidly stable flow field, scaling flow time does not affect the solid thermal boundary under corresponding dimensionless time. Within the application scope, the Bi-Fo time scaling method can greatly reduce the time cost of transient conjugate heat transfer simulation while maintaining the accuracy of transient temperature analysis.

This paper presents the techniques and results of landing-site topographic mapping and rover localization using orbital, descent and rover images in the Chang’e-4 mission. High-resolution maps of the landing site are generated from orbital and descent images. Local digital elevation models and digital orthophoto maps with 0.02 m resolution are generated at each waypoint. The location of the lander is determined as (177.588° E, 45.457° S) using festure-matching techniques. The cross-site visual localization method is routinely used to localize the rover at each waypoint to reduce error accumulation from wheel slippage and IMU drift in dead reckoning. After the first five lunar days, the rover travels 186.66 m from the lander, according to the cross-site visual localization. The developed methods and results have been directly utilized to support the mission’s operations. The maps and localization information are also valuable for supporting multiple scientific explorations of the landing site.