The lunar nearside has been investigated by many uncrewed and crewed missions, but the farside of the Moon remains poorly known. Lunar farside exploration is challenging because maneuvering rovers with efficient locomotion in harsh extraterrestrial environment is necessary to explore geological characteristics of scientific interest. Chang’E-4 mission successfully targeted the Moon’s farside and deployed a teleoperated rover (Yutu-2) to explore inside the Von Kármán crater, conveying rich information regarding regolith, craters, and rocks. Here, we report mobile exploration on the lunar farside with Yutu-2 over the initial 2 years. During its journey, Yutu-2 has experienced varying degrees of mild slip and skid, indicating that the terrain is relatively flat at large scales but scattered with local gentle slopes. Cloddy soil sticking on its wheels implies a greater cohesion of the lunar soil than encountered at other lunar landing sites. Further identification results indicate that the regolith resembles dry sand and sandy loam on Earth in bearing properties, demonstrating greater bearing strength than that identified during the Apollo missions. In sharp contrast to the sparsity of rocks along the traverse route, small fresh craters with unilateral moldable ejecta are abundant, and some of them contain high-reflectance materials at the bottom, suggestive of secondary impact events. These findings hint at notable differences in the surface geology between the lunar farside and nearside. Experience gained with Yutu-2 improves the understanding of the farside of the Moon, which, in return, may lead to locomotion with improved efficiency and larger range.

Abstract. The Chang'e-4 successfully landed on the far side of the moon in January 2019. By the 12th lunar day, its Yutu-2 rover had achieved a breakthrough travel distance of greater than 300 m. A visible and near-infrared imaging spectrometer (VNIS), consisting of a visible and near-infrared (VNIR) imaging spectrometer and a shortwave infrared (SWIR) spectrometer was used for detecting mineralogical compositions of lunar-surface materials. Because VNIS is fixed on the front of the rover, and the field-of-view (FOV) of VNIR and SWIR are small (8.5° and 3.6° respectively), approaching and accurately pointing at the specific science target depend completely on the precise control of the moving rover.In this paper, a successful method of VNIS target detection based on vision measurement is proposed. First, the accurate position of the target is calculated via navigation camera imaging. Then, the moving path is planned by considering the terrain environment, illumination, communication condition, and other constraints. After the rover moves to the designed position, the binocular imaging of the hazard-avoidance cameras are activated, the detection direction and forward distance are calculated according to the images, and the FOV trajectory of the VINS is predicted while moving. Finally, by choosing the required moving control parameters, the imaging field of the VINS accurately cover the detected targets visually.These methods have been verified many times, and the results show that they are effective and feasible. The research results based on the VNIS data have successfully revealed the material composition on the far side of the moon and have deepened human understanding of its formation and evolution.

Abstract. Bio-inspired polarization navigation is a promising navigation method inspired by insects’ autonomous foraging and homing behaviour. Many insects acquire their spatial orientation by sensing the polarization pattern of the skylight. We propose utilization of solar meridian in the polarized skylight as an orientation cue because of its significant features. Using its features, we then design and construct an imaging polarization navigation prototype. The prototype consists of a field-division polarization imaging sensor, the corresponding software, an interface, and the solar-meridian recognizing and measurement algorithm. The field-division polarization imaging sensor is the core component of the prototype and acquires polarized intensity images. To adapt to the demand of real-time on navigation system, we then propose an optimized real-time polarization image processing and pattern recognition algorithm based on Hough transform. The azimuth measurement accuracy of the sensor is then calibrated using a facility that is able to get higher azimuth accuracy by measurement of the star light. To verify the navigation capability of the developed system, we use a dynamic experiment, where the prototype is installed on the top of a vehicle and its navigation performance is compared with GNSS.