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Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

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posted on 11.04.2022, 14:52 authored by Marco ViscaMarco Visca, Roger PowellRoger Powell, Yang GaoYang Gao, Saber FallahSaber Fallah
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.

Funding

Grant Agreement No 101052200—EUROfusion

History

Email Address of Submitting Author

mv00282@surrey.ac.uk

ORCID of Submitting Author

0000-0001-8497-8302

Submitting Author's Institution

University of Surrey

Submitting Author's Country

United Kingdom