On Predicting Terrain Changes Induced by Mobile Robot Traversal

Mobile robots operating in convoys have a limited view of the terrain to be traversed if it is occluded by the preceding vehicle. Furthermore, the preceding vehicle might change the terrain geometry and eventually significantly alter its traversability by driving over the terrain. When the following vehicles do not consider such changes, they can use spurious terrain appearance and geometry to decide whether to follow in the tracks of the previous vehicle or to avoid them since the preceding vehicle’s tracks can make the terrain untraversable. We propose to predict the terrain changes induced by the robot traversal on the traversed terrain and thus support the decision-making of the following vehicles. The developed model projects the robot wheel footprint along the planned robot path and combines the projection with the terrain appearance and prior terrain elevation. The coupled model is used in a convolutional neural network that predicts the elevation after traversal. The footprint projection component is designed so that learned networks can be transferred to vehicles with different wheel footprints without relearning the model. The proposed model is verified using a dataset captured using a real, one-ton, six-wheel robot traversing rigid roads and vegetated fields.