空间资源利用正在开启一个新的空间时代。月球南极存在水冰的科学证据,从月球风化层中提取氧气的最新进展,它作为建造避难所的材料正在定位月球,再次,在重要的太空计划的中心。这些世界性的项目,由ARTEMIS领导,预计机器人技术将成为推动人类下一次巨大飞跃的颠覆性技术。然而,月球机器人需要高度的自主性才能更有效地执行月球探测任务,而无需不断受到地球的控制。此外,拥有一个以上的机器人系统将增加全球系统的弹性和鲁棒性,提高成功率,以及提供额外的冗余。本文介绍了弹性探测和月球测绘系统,开发了用于半自主月球测绘的可扩展架构。它利用多个漫游车上的视觉同时定位和映射技术来绘制大型月球环境。实施了几种复原机制,例如双代理冗余,延迟不变通信,多主体系结构不同的控制模式。这项研究提出了REALMS的实验结果与两个机器人和它的潜力被扩大到更多的机器人,增加地图覆盖范围和系统冗余。该系统的性能在月球模拟设施中进行了验证和验证,以及在欧洲航天局(ESA)-欧洲空间资源创新中心空间资源挑战赛期间更大的月球环境。不同实验的结果表明了REALMS的效率和使用半自治系统的好处。
Space resource utilisation is opening a new space era. The scientific proof of the presence of water ice on the south pole of the Moon, the recent advances in oxygen extraction from
lunar regolith, and its use as a material to build shelters are positioning the Moon, again, at the centre of important space programs. These worldwide programs, led by ARTEMIS, expect robotics to be the disrupting technology enabling humankind\'s next giant leap. However, Moon robots require a high level of autonomy to perform
lunar exploration tasks more efficiently without being constantly controlled from Earth. Furthermore, having more than one robotic system will increase the resilience and robustness of the global system, improving its success rate, as well as providing additional redundancy. This paper introduces the Resilient Exploration and Lunar Mapping System, developed with a scalable architecture for semi-autonomous
lunar mapping. It leverages Visual Simultaneous Localization and Mapping techniques on multiple rovers to map large
lunar environments. Several resilience mechanisms are implemented, such as two-agent redundancy, delay invariant communications, a multi-master architecture different control modes. This study presents the experimental results of REALMS with two robots and its potential to be scaled to a larger number of robots, increasing the map coverage and system redundancy. The system\'s performance is verified and validated in a
lunar analogue facility, and a larger
lunar environment during the European Space Agency (ESA)-European Space Resources Innovation Centre Space Resources Challenge. The results of the different experiments show the efficiency of REALMS and the benefits of using semi-autonomous systems.