The potential of applying the geometry-based rover slip prediction model learned on earth to the moon
Wheeled ground robots are widely used in planetary exploration missions, where slip can measure problems during driving and pre-acquisition slips play an important role. The current best traditional model is a predictive model based on visual sliding learning, which is divided into two steps, determining the terrain type and constructing a geometry-based sliding model. Since the geometry-based sliding mode model is not realistic in the orbit learning, this paper introduces the possibility of applying geometric models to planetary missions. In order to conduct a more comprehensive study of geometry-based models, three new related variables were introduced. Then the geometric model-based rain map rover prototype data obtained from the indoor experiment is applied to the rain map rover data in the Lunar Changchun No. 3 mission. The results show that terrain geometry is the main influencing factor when the simulation of local shaped materials reaches a certain level. This means that the geometry-based slip prediction model has the potential to be applied under similar conditions, and the results of the article have certain value. At the same time, this method provides another way of thinking for China’s future Mars project.
Keywords
Slip prediction,
Potential analysis,
Geometry-based model,
Planetary rovers,
CE-3 missionAuthors
| Hao Ma | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences; College of Resources and Environment, University of Chinese Academy of Sciences | mahao@radi.ac.cn |
| Chen Qi | School of Geodesy and Geomatics, Wuhan University | qc51372@gmail.com |
| Qunzhi Li | Beijing Institute of Spacecraft System Engineering, Chinese Academy of Space Technology | 13681332025@139.com |
| Deli Meng | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences; College of Resources and Environment, University of Chinese Academy of Sciences | mengdl@radi.ac.cn |
| Shaochuang Liu | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences | mahao123321@yeah.net |
Всего: 5
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