Improving Slip Prediction on Mars Using Thermal Inertia Measurements

Abstract

Abstract-Rovers operating on Mars have been delayed, diverted, and trapped by loose granular materials. Vision-based mobility prediction approaches often fail because hazardous sand is difficult to distinguish from safe sand based on surface 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 showing that considering thermal inertia improves rover slip prediction on Mars using in-situ 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. These results support the hypothesis that the consideration of thermal inertia improves mobility estimates for rovers on Mars.

Safe Robot Navigation Via Multi-Modal Anomaly Detection

“…Semantic-aware navigation approaches typically leverage additional sensor modalities to infer additional terrain information [13], [14], [15], [16], [17], [18], [2], [19], [20]. Approaches using more unconventional sensors either require a long observation duration [19] or a bulky sensor payload [20] which exceeds the capabilities of our target platform.…”

“…Semantic-aware navigation approaches typically leverage additional sensor modalities to infer additional terrain information [13], [14], [15], [16], [17], [18], [2], [19], [20]. Approaches using more unconventional sensors either require a long observation duration [19] or a bulky sensor payload [20] which exceeds the capabilities of our target platform. Therefore, most work is focused on camera-based methods and either performs semantic segmentation of the environment [13], [15], [16], [2] or directly predicts a traversability label [14], [17], [18].…”