3 accepted papers
Accurate estimation of wheel–terrain interaction parameters is important for efficient navigation of Unmanned Ground Vehicles in unstructured outdoor environments. In this paper, we propose a hybrid data-driven and model-based method to estimate a priori emph{motion resistance}, a terrain-specific p…
Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a modern Reinforcement Learning framework to reduce trai…
This paper introduces a novel feature detector based only on information embedded inside a CNN trained on standard tasks (e.g. classification). While previous works already show that the features of a trained CNN are suitable descriptors, we show here how to extract the feature locations from the ne…