2020-07-242020-07-242020-07-31ICES_2020_288https://hdl.handle.net/2346/86274Matthias Killian, Technical University of Munich, DEICES102: Thermal Control for Planetary and Small Body Surface MissionsThe proceedings for the 2020 International Conference on Environmental Systems were published from July 31, 2020. The technical papers were not presented in person due to the inability to hold the event as scheduled in Lisbon, Portugal because of the COVID-19 global pandemic.In the near future, solar-powered rovers are expected to explore the poles of the Moon. The simulation tool Thermal Moon Simulator for Exploration (TherMoS X), currently under development at the Chair of Astronautics at the Technical University of Munich, is now able to optimize traverses of a rover on the lunar surface. As a novelty, TherMoS X simulates the full energy state of the rover including its thermal state. The updated thermal model of the Moon determines temperatures of the surface of the Moon, which show a Pearson correlation coefficient of 0.955 if compared to Diviner. Analyses in this paper focus on a solar-powered rover with a rechargeable battery and power consumption simulated in specific domains. A thermal model of the rover including a resistive heating element completes the simulation approach. An adapted version of the optimization algorithm A* determines near-optimal solutions of traverses along waypoints while simulating the energy state of the rover. Traverse optimization is carried out at two different sites in the south polar region of the Moon with a precise terrain model of co-registered data from LOLA. The investigated areas are 30 km by 30 km. Results show that in both scenarios a rechargeable battery is required to find a traverse that is navigable under the given boundary conditions. At one site, the traverse with the lowest battery capacity possible differs significantly from the ones that result from optimization with the classic approach A* where the energy state of the rover is neglected. Total distance is slightly longer by 18.1 % but the battery capacity is 86 % less than the one needed to follow the shortest traverse determined by the original A* algorithm. At the other site, no difference in traverses occurs mainly because waypoints are positioned close by and illumination conditions are benign.application/pdfengMoonLunar surfaceThermalRoverTraverseOptimizationEnergy statePresentation