文献类型：期刊文献

英文题名：Learning to navigate on the rough terrain: A multi-modal deep reinforcement learning approach

作者：Zhou, Bo[1]; Yi, Jianjun[1]; Zhang, Xinke[1]

机构：[1] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

年份：2022

起止页码：189

外文期刊名：2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems, ICPICS 2022

收录：EI(收录号：20223912800349)

语种：英文

外文关键词：Air navigation - Data fusion - Deep learning - Landforms - Learning systems - Modal analysis - Off road vehicles - Unmanned vehicles

摘要：How to enable safe navigation of unmanned vehicles on complex and rough terrain is challenging and meaningful research. In this paper, we propose an end-to-end reinforcement learning local navigation method with multi-modal data fusion, which effectively combines the intrinsic perception, such as Inertial Measurement Unit (IMU) measurements, and the extrinsic perception, such as Three-dimensional (3D) point clouds and images, of an unmanned vehicle. A specific feature extraction network is constructed for each modal data, and the total network is effectively trained using a modal separation learning method. The experimental results show that the proposed method can effectively address various obstacles such as rough roads, vegetation obstacles, and water pool disturbances to achieve autonomous and safe navigation of unmanned vehicles in off-road scenarios. ? 2022 IEEE.