Topography Of Lunar Surface Research Articles

Preliminary design of lunar high-resolution remote sensing micro-nano satellite

The development and utilization of lunar resources and the construction of lunar scientific research stations are important parts of China’s future lunar exploration plan. Aiming at the problems of China’s micro-nano satellites with low radiation resistance, lack of autonomous Earth-Moon transfer capability, and limited optical system volume and quality, a preliminary design scheme of a lunar remote sensing micro-nano satellite with autonomous Earth-Moon transfer capability is proposed. Using the analytical orbit average technology and transcription and collocation method to calculate the satellite’s Earth-Moon transfer orbit, the satellite can use the Hall thruster with large specific impulse and low thrust to realize the autonomous Earth-Moon transfer. The passive + active method is used to optimize the radiation resistance of the system, and the total radiation dose of some single machines can reach more than 25krad. A compact high-resolution multispectral camera is designed, and imaging simulation and verification are carried out. In the end, the satellite can achieve high-resolution imaging of the moon with a resolution of 0.2 m at an orbital height of 100 km to achieve the purpose of fine exploration of information such as lunar surface topography and geological structure.

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints.

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.

Dielectric Inversion of Lunar PSR Media with Topographic Mapping and Comment on “Quantification of Water Ice in the Hermite-A Crater of the Lunar North Pole”

Dielectric inversion of lunar permanently shadowed region (PSR) of moon poles has been studied for estimation of possible water–ice content. The Campbell model was directly applied to mini-SAR data for inversion on the Hermite-A crater region. However, this letter presents quantitative analysis that the lunar surface topography, i.e., surface roughness and slopes, and underlying dielectric media, and so on, can significantly affect this inversion. The model is actually degenerated into a half-space model without topographic account. This letter presents a two-layer model of Kirchhoff-approximation surface/small perturbation approximation subsurface to take account of all these topographic factors for PSR dielectric inversion.