  Driving energy consumption plays a major role in the navigation of mobile
robots in challenging environments, especially if they are left to operate
unattended under limited on-board power. This paper reports on first results of
an energy-aware path planner, which can provide estimates of the driving energy
consumption and energy recovery of a robot traversing complex uneven terrains.
Energy is estimated over trajectories making use of a self-supervised learning
approach, in which the robot autonomously learns how to correlate perceived
terrain point clouds to energy consumption and recovery. A novel feature of the
method is the use of 1D convolutional neural network to analyse the terrain
sequentially in the same temporal order as it would be experienced by the robot
when moving. The performance of the proposed approach is assessed in simulation
over several digital terrain models collected from real natural scenarios, and
is compared with a heuristic inclination-based energy model. We show evidence
of the benefit of our method to increase the overall prediction r2 score by
66.8% and to reduce the driving energy consumption over planned paths by 5.5%.
