Rovers operating on Mars have been delayed, diverted, and trapped by loose granular materials. Vision-based mobility prediction cannot reliably distinguish hazardous sand from safe sand based on appearance alone. Unlike surface appearance, the thermal inertia of terrain is directly correlated to the same geophysical properties that control slip. This paper presents a quantitative analysis that shows improvement in rover slip prediction when considering thermal inertia based on data from the Curiosity rover. Thermal inertia is estimated for each slip measurement in sand using both on-board and orbital instruments. Slip models are learned using a mixture of experts approach where the experts are identified using thermal inertia. Two-expert models are compared to a single-expert, vision-only model to show that slip predictions are improved by separating high-slip, low thermal inertia sand from low-slip, high thermal inertia sand. Simulated experiments are also presented to show that thermal inertia has the potential to identify sand even when it is beneath a thin layer of surface duricrust. These results support the hypothesis that the consideration of thermal inertia improves mobility estimates for rovers on Mars.

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.

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.

Robotic missions could soon explore permanently shadowed craters on the lunar poles in order to characterize ice accumulation beneath the surface. However, the regolith in these craters is hypothesized to be very loose and could endanger a rover mission. This work analyzes the ability of thermal imaging to detect hazardous, low-density regolith in shadowed regions on the lunar poles. A series of simulations was conducted to estimate the surface temperature of lunar regolith as a function of density in polar craters. A generalized lunar crater model was used, and thermal properties of regolith were taken from experiments on Apollo samples. Results showed that in most situations there is a difference in temperature between nominal and loose regolith samples. This effect is most consistent at night in the absence of solar radiation and generally causes temperature differences between 2 K and 3 K. Based on comparisons to the capabilities of the DIVINER lunar radiometer, it is likely that regolith density differences would be detectable by a rover-mounted instrument.

This paper presents Soil Property and Object Classification (SPOC), a novel software capability that can visually identify terrain types (e.g., sand, bedrock) as well as terrain features (e.g., scarps, ridges) on a planetary surface. SPOC works on both orbital and ground-bases images. Built upon a deep convolutional neural network (CNN), SPOC employs a machine learning approach, where it learns from a small volume of examples provided by human experts, and applies the learned model to a significant volume of data very efficiently. SPOC is important since terrain type is essential information for evaluating the traversability for rovers, yet manual terrain classification is very labor intensive. This paper presents the technology behind SPOC, as well as two successful applications to Mars rover missions. The first is the landing site traversability analysis for the Mars 2020 Rover (M2020) mission. SPOC identifies 17 terrain classes on full-resolution (25 cm/pixel) HiRISE (High Resolution Imaging Science Experiment) images for all eight candidate landing sites, each of which spans over ∼ 100km. The other application is slip prediction for the Mars Science Laboratory (MSL) mission. SPOC processed several thousand NAVCAM (Navigation camera) images taken by the Curiosity rover. Predicted terrain classes were then correlated with observed wheel slip and slope angles to build a slip prediction model. In addition, SPOC was integrated into the MSL downlink pipeline to automatically process all NAVCAM images. These tasks were impractical, if not impossible, to perform manually. SPOC opens the door for big data analysis in planetary exploration. It has a promising potential for a wider range of future applications, such as the automated discovery of scientifically important terrain features on existing Mars orbital imagery, as well as traversability analysis for future surface missions to small bodies and icy worlds.

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.

This paper presents a method to predict soil traversability by estimating the thermal diffusivity of terrain using a moving, continuous-wave laser. This method differentiates between different densities of the samematerial, which visionbased methods alone cannot achieve. The bulk density of a granular material has a significant effect on both its mechanical behavior and its thermal properties. This approach fits the thermal response as effected by a laser to an analytical model that is dependent on thermal diffusivity. Experimental soil strength measurements validate that thermal diffusivity is a predictor of traversability for a given material.

While planetary pits and caves have been fiction for a century, they have been seen from orbit only in the last few years. These discoveries exceed the fantasies in diversity, scale, and abundance. For pits and caves, this is the age of discovery, ranging from a few pits on the Moon and Mars in 2009 to hundreds within the time of this research, with many more to come. Pits with subsurface voids have been confirmed on the Moon and Mars and indicated on Venus, Phobos, Eros, Gaspra, Ida, Enceladus, and Europa. Compelling next steps are surface and subsurface exploration.Pits and caves are opportunistic study targets for unique origins, geology, and climate that will broadly impact planetary science. Holes on Mars are of particular interest because their interior caves are relatively protected from the harsh surface, making them good candidates to contain Martian life. Pits are prime targets for possible future spacecraft, robots, and even human interplanetary explorers. Caves and caverns could be ready-_made shelters for future Moon and Mars explorers and colonists. Discoveries to date look down from on high with satellites but cannot reveal the wonders of caves. They cannot enter, touch, or view pits up close. Genuine exploration is only achievable through surface missions. Robotic missions can assess suitability for safe entry and habitation, plus inform techniques for developing subsurface infrastructure.Missions into planetary voids redefine the future of exploration, science, and habitation beyond Earth. We can reach this future only by targeting specific technological advancement now. Prior missions and current roadmap priorities target regions of benign terrain. While in-cave concepts have been postulated, the critical technologies have not been identified and demonstrated.While robotic exploration of skylights and caves can seek out life, investigate geology and origins, and open the subsurface of other worlds to humankind, it is a daunting venture. Planetary voids present perilous terrain requiring innovative technologies for access, exploration, and modeling. These same technologies are broadly applicable to explorations of rough and/or subsurface planetary environments, including caves, craters, cliffs, and rock fields. This research speculates on the possibilities and means of such exploration with fundamental contributions to exploring, modeling, and visualizing this new class of large-scale, highly three-dimensional concave planetary features.

The fidelity of many inertial Body Sensor Network (BSN) applications depends on accurate spatio-temporal information retrieved from body-worn devices. However, there are many challenges caused by inherent sensor errors in inertial BSNs and the uncertainty of dynamic human motion in various situations, such as integration drift and mounting error. Spatial information is especially difficult to extract from inertial data. This paper presents practical methods to minimize errors caused by these challenges within the context of a case study -- gait speed estimation -- where both temporal and spatial information are crucial for accuracy. These methods include a practical calibration procedure for correcting mounting error in order to obtain more accurate spatial information and a refined human gait model for more accurate temporal information.