Multiple Rover Efficient Automated Mapping

Stottler Henke’s Multiple-Rover Efficient Automated Mapper (M-REAM) will provide the highest resolution and most frequently updated maps of the Lunar South Pole surface changes as a result of autonomous operations with minimal to zero impact on the primary science mission of those surface assets. This will bolster both the autonomous operations on the surface accuracy and science on the ground with the highest-quality digital twins of the Lunar South Pole operations. Our approach is divided into two components: an innovative 3D collaborative SLAM technique and scoring the “interestingness” of certain areas to adjust path planning algorithms. We will implement a custom-designed SLAM approach for the challenges of the Lunar South Pole from the low solar angle and long shadows. A learned Neural Radiance Field (NeRF) representation of the lunar regolith and localizations for rovers will be augmented by a Neural Visibility Field (NeRV), treating materials as reflectors of light rather than emitters. While we primarily update the lunar mapping utilizing images that a rover captures throughout its typical daily routine, we introduce a scoring mechanism for “interestingness” that allows for minimal adaptation of the rover’s primary mission to capture massive amounts of new information. For example, a RASSOR taking the same path from digging to the regolith processing site would be incentivized to take a slightly different route to update areas of the map that might be interesting. Interestingness is largely defined by autonomous operations in the area by a surface asset but is also driven by pixel-wise errors rendered by the NeRV NN, a learned time-decay function to account for meteorite impacts and other unexpected changes, or manually by a human operator. By developing, leveraging, and integrating these two cutting-edge technologies, the system will push the present boundaries of mapping on the lunar surface and enable more sophisticated science and missions.

The most direct transition target would be NASA’s VIPER landing later in 2024, but M-REAM will be compatible with all of NASA’s growing list of autonomous assets on the Lunar South Pole. It is designed to facilitate the collaboration of multiple surface operation missions. As more missions successfully soft-land, M-REAM becomes better equipped and enables the autonomous assets to work together on the lunar surface, marking areas that each one works in as interesting. Additionally, there will be more constraints on sending information back to Earth as the quantity of assets climbs, and M-REAM will manage this collection in the most efficient manner. This allows for the autonomous collection and management of the most updated navigation maps and the highest-quality and most recent maps for researchers and NASA on the ground. M-REAM is also designed within NASA’s Moon to Mars Architecture, where NASA will prove autonomous technologies, capabilities, and new technologies such as In-Situ Resource Utilization on the Moon, and then later adapt them for Mars. On Mars, M-REAM has similar abilities and benefits and will again provide the highest-quality and most frequently updated maps of the surface changes as a result of autonomous operations. Additionally, other NASA rovers and autonomous systems which have a primary mission but would benefit from inexpensive, consistently updated mapping in space or on Earth could benefit from the M-REAM technology. JPL’s Hazbot, designed for remote exploration of hazardous material release sites or JSC’s Robonaut 2, designed to be a humanoid robot for hazardous environments both operate in an applicable and beneficial domain for M-REAM. Later NASA rovers that land on asteroids would also benefit from an M-REAM implementation. Beyond the obvious lunar applications, any NASA task coordinating multiple rovers or other autonomous surface operators in dangerous or unknown environments would likely benefit from the developed M-REAM. M-REAM will be a powerful technology for any company operating autonomously on the lunar surface, and Stottler Henke would approach our collaborator and leading Lunar South Pole robotics operator Astrobotic to discuss an implementation of M-REAM on their CubeRovers, VSATs, and other autonomous operating assets. Stottler Henke and Astrobotic share a very positive business relationship: Stottler Henke has recently completed the MAIFLOWER Phase I project, which developed an anomaly detection suite for the Vertical Solar Array Technology (VSAT) rover. While CubeRover and other Astrobotic rovers will have their own primary mission and path planning objectives, utilizing this technology would open up a host of new abilities for their operations. A CubeRover could investigate another CubeRover that has stopped communicating and responding to commands, or CubeRovers could autonomously all take slightly different routes which do not impact their science missions but allow Astrobotic to get a full 360° view of their lander for landing analysis. In addition, with both NASA and Astrobotic’s rovers using M-REAM, the collaboration potential and understanding of the lunar surface will greatly increase, benefiting NASA, Astrobotic, and the community at large with more updated and better resolution data. More generally, M-REAM is applicable to a variety of dangerous and dynamic situations with the need to be monitored and mapped inexpensively while performing a primary mission. A Spot Robotic Dog investigating and fixing a stuck valve in a nuclear power plant could perform its primary mission, navigating to the valve and manually turning it open with the arm attachment. Simultaneously, it could utilize M-REAM’s interestingness and SLAM algorithms to keep available the most updated and accurate map of the power plant. For any dangerous factory, oil rig, mining operation, M-REAM would provide invaluable and important information.

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