REALMS2 -- Resilient Exploration And Lunar Mapping System 2 -- A Comprehensive Approach

Abstract-- The European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) created the Space Resources Challenge to invite researchers and companies to propose innovative solutions for Multi-Robot Systems (MRS) space prospection. This paper proposes the Resilient Exploration And Lunar Mapping System 2 (REALMS2), a MRS framework for planetary prospection and mapping. Based on Robot Operating System version 2 (ROS 2) and enhanced with Visual Simultaneous Localisation And Mapping (vSLAM) for map generation, REALMS2 uses a mesh network for a robust ad hoc network. This system is designed for heterogeneous multi-robot exploratory missions, tackling the challenges presented by extraterrestrial environments. REALMS2 was used during the second field test of the ESA-ESRIC Challenge and allowed to map around 60% of the area, using three homogeneous rovers while handling communication delays and blackouts. Recently, the Moon has regained the focus of space agencies and private companies for potential In-Situ Resources Utilisation (ISRU). Therefore, the European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) seek to increase the level of autonomy of robotic systems used for the exploration of space resources. ESA and ESRIC organised the Space Resources Challenge [1], where 13 research teams competed in a first field test to demonstrate their concepts of autonomous systems, leveraging the advantages of Multi-Robot Systems (MRS). The five best teams continued to a second field test [2] with the task of finding different resources within a large lunar analogue environment, shown in Fig 1. During the first field test of the Challenge [2], the authors present the Resilient Exploration And Lunar Mapping System (REALMS) [3], a MRS using two rovers mapping the environment with Visual Simultaneous Localisation And Mapping (vSLAM). This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant references 14783405, 17025341 and 17679211.