Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR - Sánchez-Montero, Manuel; Morales-Rodríguez, Jesús; Martínez-Rodríguez, Jorge Luis (MDPI, 2023)
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.
Keywords: Robótica; Robots autónomos; Aprendizaje automático (Inteligencia artificial); 3D LiDAR; Reinforcement learning; Off-road navigation; Curriculum learning; Unmanned ground vehicles; Traversability; Robotic simulations