space mission engineers

Abstract: This paper present a spiral-cavity wheel for lunar regolith excavation and a sensor-light evaluation stack that jointly estimates fill ratio (vision), sinkage (vision), and specific energy from actuator logs. In benchtop tests (four revolutions at 5, 10, and 15~RPM) against two literature baselines, the proposed wheel achieved higher excavated mass and fill ratio, delivering 2.2-3.0 times higher excavation rate while reducing specific energy by 29 % relative to a bucket-drum baseline. Normalized sinkage (mm/kg) was also lower, indicating stable traction without bogging. Effort-time traces show a steady torque envelope with repeatable cut-carry-dump cycles across speeds. We provide a retention index $η$ that correlates with fill ratio and a DEM setup that reproduces experimental trends with low error. Results suggest spiral-cavity wheels can replace heavier multi-actuator diggers when mass, simplicity, and energy efficiency are mission drivers.

Abstract: Autonomous navigation on Mars requires vehicles to distinguish between traversable terrains across diverse and visually challenging environments. However, progress in learning-based navigation for off-world environments has been limited by the lack of large-scale datasets. Since landing in Jezero Crater, the Mars 2020 Perseverance rover has traversed terrain ranging from sandy dunes, rocky patches, and flat bedrocks. As a result, this paper presents a dataset spanning 500 sols and 45km of trajectories driven by both human operators and the onboard planner, ENav. Our dataset contains grayscale stereo image pairs, poses, accelerometer readings, rocker-bogie angles, and estimates of tilt and wheel slip. Building on this dataset, we introduce an uncertainty-aware traversability-estimation framework that learns terrain representations from multimodal driving experience. We compare our proposed method against existing approaches on the Mars 2020 dataset and show that our method achieves an AUROC of 0.874 and an F1 score of 0.758, outperforming the strongest baseline by 0.058 and 0.156, respectively, while also achieving the highest average precision and recall. Finally, we show that the visual representations can be integrated into path planners, such as ENav, on a physical rover test bed. Videos, code, and the M2020 dataset will be available at https://darren-chiu.github.io/learning-to-drive-on-mars.