TY  - JOUR
PY  - 2023//
TI  - Reinforcement and curriculum learning for off-road navigation of an UGV with a 3D LiDAR
JO  - Sensors (Basel)
A1  - Sanchez, Manuel
A1  - Morales, Jesús
A1  - Martínez, Jorge L.
SP  - 
EP  - 
VL  - 23
IS  - 6
N2  - 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>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s23063239
ID  - ref1
ER  -