REALMS: Resilient exploration and lunar mapping system

• Published in: Frontiers in robotics and AI 2023-03, Vol.10, p.1127496-1127496
• Main Authors: van der Meer, D, Chovet, L, Bera, A, Richard, A, Sánchez Cuevas, Pedro Jesus, Sánchez-Ibáñez, J R, Olivares-Mendez, M
• Format: Article
• Language: English
• Subjects: delay invariant; lunar; mapping; multi-master; resilience; Robotics and AI; VSLAM
• ISSN: 2296-9144; 2296-9144
• DOI: 10.3389/frobt.2023.1127496

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.