Future robotic missions to the poles of the Moon and Mercury will face challenges not encountered by current and prior planetary rover missions. Careful energy-aware spatiotemporal path planning will be required to accomplish mission objectives at high cadence under changing illumination conditions. With attention to landing site and time, such spatiotemporal path planning may enable extended missions on order of months that would not otherwise be possible. This work presents improvements in energy-aware spatiotemporal path planning for multiple waypoints to significantly reduce planning time. A time-compression technique is used to simplify planning in areas where changes occur infrequently. Consideration of end-goal reachability reduces the search space. Finally, heuristics that use pre-computation with static obstacles speed up the search.

This paper presents a method for predicting slip using Gaussian process regression. Slip models are learned for visually classified terrain types as a function of terrain geometry. Spatial correlations between terrain properties are leveraged for on-line slip model adaptation. Results show that regression-based modeling using in-situ rover data outperforms the state-of-practice, terrestrially-calibrated slip curves in both mean prediction and uncertainty bounds. Local adaptation improves slip prediction results, particularly in high-slip sand areas that pose the greatest threat to rovers. Slip estimates made using a visual classifier to identify terrain type are compared to estimates using on-line model selection with only proprioceptive slip measurements as inputs. The proprioceptive results nearly match the visual results, showing that this approach could work even when a visual classifier is not available.

The inability of current robotic perception techniques to adequately detect non-geometric terrain hazards is a primary cause of failure for robots operating in natural terrain on Mars, the Moon, and Earth. Classical approaches detect surface appearance but do not measure the underlying mechanical properties that determine wheel-terrain interaction. Diurnal temperature variations of a granular material, however, are strongly correlated with both its surface appearance and subsurface geophysical properties. This paper presents a technique for determining relative differences in looseness and traversability of granular terrain through analysis of thermal imagery. Terrain compaction and traversability are predicted by estimating a material's thermal inertia from observations of thermal transients. Results from a set of experiments in sandy terrain demonstrate the ability of this approach to differentiate between safe, compact and hazardous, loose terrain.