Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground
Vehicles in Unknown Terrains
Abstract
This paper presents an adaptive energy-aware prediction and planning
framework for vehicles navigating over terrains with varying and unknown
properties. A novel feature of the method is the use of a deep
meta-learning framework to learn a prior energy model, which can
efficiently adapt to the local terrain conditions based on small
quantities of exteroceptive and proprioceptive data. A meta-adaptive
heuristic function is also proposed for the integration of the energy
model into an A* path planner. The performance of the proposed approach
is assessed in a 3D-body dynamic simulator over several typologies of
deformable terrains, and compared with alternative machine learning
solutions. We provide evidence of the advantages of the proposed method
to adapt to unforeseen terrain conditions, thereby yielding more
informed estimations and energy-efficient paths, when navigating on
unknown terrains.
Submitted for revision to IEEE Transaction on Cybernetics.