Data‐driven mobility risk prediction for planetary rovers

Abstract

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 roverterrain conditions. The combination of HSF with an appropriately sized queue structure for modeling slip versus slope enables an appropriate balance between responsiveness and stability. K E Y W O R D S mobility prediction, planetary robotics, rover slip

Proprioceptive Slip Detection for Planetary Rovers in Perceptually Degraded Extraterrestrial Environments

“…Due to greater compaction resistance, the rover wheels suffer sinkage-related slippage while traversing such terrains. A recent research has focused on exploiting the relation between slope and slip using proprioceptive sensors along with exteroceptive data [Skonieczny et al, 2019]. Although slope has a strong effect on slippage, wheel slippage may also be observed in relatively flat terrains if the rover wheels encounter kinematic incompatibility .…”

Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization

“…Moreover, the wheel-terrain interactions (terramechanics) are not dictated by the visible topsoil of the terrain [5]. To address this, a recent line of research has focused on datadriven cubic regression metrics to predict slip with respect to the slope by using proprioceptive and exteroceptive sensors [18]. Although slippage is strongly affected by increasing absolute value of a slope, wheel slippage can also be observed on flat terrains while encountering local obstacles (e.g., small rocks that rover can traverse on) due to kinematic incompatibility [13].…”

Terrain-Dependent Slip Risk Prediction for Planetary Exploration Rovers

“…Skonieczny et al 14 evaluated the trafficability risk of planetary rovers using a slip-slope table specific to each terrain, which adapts to variable slip versus slope data, and by assigning thresholds between low, medium, and high slip risk. These thresholds were defined by considering the hazardous slip ratio to be 0.2; however, these fixed thresholds could cause incorrect wheel slip risk evaluation owing to the uncertainty of slope estimation.…”