The motors or engines of an autonomous ground vehicles (AGV) have torque and power limitations, which limit their abilities to climb steep hills, which are defined to be hills that have high grade sections in which the vehicle is forced to decelerate. Traversal of a steep hill requires the vehicle to have sufficient momentum before entering the hill. This problem is part of a larger class of momentum-based motion planning problems such as the problem of lifting heavy objects with manipulators. Hence, solutions to the steep hill climbing problem have much wider applicability. The motion planning here is accomplished using a dynamic model of the skid-steered AGV used in the experiments along with Sampling Based Model Predictive Control (SBMPC), a recently developed input sampling planning algorithm that may be viewed as a generalization of LPA* to the direct use of kinodynamic models. The motion planning is demonstrated experimentally using two scenarios, one in which the robot starts at rest at the bottom of a hill and one in which the robot starts at rest a distance from the hill. The first scenario requires the AGV to first reverse direction so that the vehicle can gather enough momentum before reaching the hill. This corresponds to having the vehicle begin at a local minimum, which results in a problem that many traditional model predictive control methods cannot solve. It is seen that, whereas open loop trajectories can lead to vehicle immobilization, SBMPC successfully uses the information provided by the dynamic model to ensure that the AGV has the requisite momentum.