Stochastic prediction of remaining driving time and distance for a planetary rover

The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation of the rover, it is important to make reliable predictions regarding the end-of-discharge time, which, in turn, can be used to estimate the remaining driving time and distance of the rover. In addition, quantifying the uncertainty in these predictions is critical to making risk-informed decisions regarding the operations of the rover. This paper presents a computational methodology to stochastically predict end-of-discharge time, remaining driving time, and remaining driving distance for a planetary rover, based on monitoring the batteries that power the rover. We utilize a model-based prognostics framework that characterizes and incorporates the various sources of uncertainty into these predictions, thereby assisting operational decision-making. We consider two different types of driving scenarios, structured and unstructured driving, and characterize the uncertainty they create in the future usage of the rover. In structured driving, the rover navigates among a set of known waypoints, and in unstructured driving, the rover performs a sequence of unplanned maneuvers. Results from a set of field experiments illustrate these computational methods and demonstrate their applicability.