Event Location: GHC 2109

Abstract: Loose, granular terrain can cause rovers to slip and sink, inhibiting mobility and sometimes even permanently entrapping a vehicle. Traversability of granular terrain is difficult to foresee using traditional, non-contact sensing methods, such as cameras and LIDAR. This inability to detect loose terrain hazards has caused significant delays for rovers on both the Moon and Mars and, most notably, contributed to Spirit’s permanent entrapment in soft sand on Mars. These delays are caused both by slipping in unidentified loose sand and by wasting time analyzing or completely circumventing benign sand. Reliable prediction of terrain traversability would greatly improve both the safety and the operational speed of planetary rover operations. This thesis leverages thermal inertia measurements and physics-based terramechanics models to develop algorithms for slip prediction in planetary granular terrain.

The ability of a rover to traverse granular terrain is a complex function of the geometry of the terrain, the rover’s configuration, and the physical properties of the granular material, such as density and particle geometry. Vision-based traversability prediction methods are inherently limited. Subsurface characteristics are not exclusively correlated with visual appearance of the surface layer. Vision does not provide enough information to fully understand all the physical properties that influence mobility. The inherent difficulty of estimating traversability is compounded by the conservative nature of planetary rover operations. Mission operators actively avoid potentially hazardous regions, which makes strictly data-driven regression approaches difficult due to limited data.

Pre-proposal research has shown that thermal inertia is correlated to and improves estimates of traversability. This has been demonstrated both in terrestrial experiments and by using data from the Curiosity rover. Unlike visual appearance, thermal properties of a material are not only influenced by the surface of terrain but also by the physical properties of the underlying material. This thesis develops techniques for predicting the traversability of terrain by leveraging thermal inertia measurements to provide a greater understanding of material properties both at and below the surface.

The proposed research will develop computationally efficient traversability prediction technologies. Thermal inertia and geometric features, such as angle of repose, will be used to estimate granular terrain properties. Then surface geometry and soil parameters will be used as inputs to a learning-based slip prediction algorithm. The algorithm will be trained on both in-situ and synthetic data to reduce overfitting and increase prediction accuracy. Synthetic data will be generated using state-of-the-art terramechanics simulators that produce accurate slip estimates given known terrain properties but are too computationally inefficient to be used for tactical rover planning. Evaluation will occur on data from the Mars rovers. Results will be compared to vision-only methods in order to understand in what situations the addition of thermal inertia can improve traversability prediction.

Committee:William “Red” Whittaker, Chair

David Wettergreen

Steven Nuske

Issa Nesnas, Jet Propulsion Laboratory