@article{ref1,
title="Reinforcement and curriculum learning for off-road navigation of an UGV with a 3D LiDAR",
journal="Sensors (Basel)",
year="2023",
author="Sanchez, Manuel and Morales, Jesús and Martínez, Jorge L.",
volume="23",
number="6",
pages="-",
abstract="This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor-Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23063239",
url="http://dx.doi.org/10.3390/s23063239"
}