Data-driven mobility risk prediction for planetary rovers

Mobility assessment and prediction continues to be an important and active area of research for planetary rovers, with the need illustrated by multiple examples of high slip events experienced by rovers on Mars. Despite slip versus slope being one of the strongest and most broadly used relationships in mobility prediction, this relationship is nonetheless far from precisely predictable. Although the literature has made significant advances in the predictability of average mobility, the other key related aspect of the problem is the risk caused by edge cases. A key contribution of this study is a metric for explicitly assessing mobility risk based on data-driven nonparametric slip versus slope relationships. The data-driven approach is meant to address limitations of past model-based approaches. The metric is informed by past work in terramechanics relating drawbar pull (i.e., net traction) to slip: High slip fraction (HSF), defined as the proportion of slip data points above 20%. Another contribution is a low complexity mobility prediction framework, the autonomous soil assessment system. Field tests demonstrate that, for sand and gravel, rover trafficability becomes nonlinear and highly variable above the 20% slip threshold. HSF is shown to be a useful metric for categorizing rover-terrain interactions into low, medium, or high risk, correctly and consistently. Furthermore, the metric is shown to be useful for early detection of potentially hazardous changes in rover-terrain conditions. The combination of HSF with an appropriately sized queue structure for modeling slip versus slope enables an appropriate balance between responsiveness and stability.