Institute for Mechatronics Engineering and Cyber-Physical Systems, Universidad de Málaga, 29071 Málaga, Spain.
Sensors (Basel). 2023 Mar 18;23(6):3239. doi: 10.3390/s23063239.
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.
本文提出了一种使用深度强化学习（RL）来实现无人地面车辆（UGV）自主导航的方法，该车辆配备了车载三维（3D）激光检测和测距（LiDAR）传感器，可在越野环境中使用。在训练过程中，同时应用了机器人模拟器 Gazebo 和课程学习范例。此外，选择了一种具有合适状态和自定义奖励函数的基于 Actor-Critic 神经网络（NN）的方案。为了将 3D LiDAR 数据作为 NN 的输入状态的一部分，开发了一个虚拟二维（2D）可遍历性扫描仪。所得到的 Actor NN 已经在真实和模拟实验中成功测试，并与同一 UGV 上的先前反应式导航方法进行了有利比较。
