Autonomous Plug-in Charging for Wheeled Mobile Manipulator in Unstructured Environments
摘要
To address the low efficiency and poor robustness of robot plug-in charging in unstructured environments, this paper proposes a method for plug-in autonomous charging via a wheeled mobile manipulator in unstructured environments. First, a two-stage paradigm target recognition and positioning algorithm named You Only Look Once version 8 (YOLOv8) and Point Pair Features (PPF) pose estimation is constructed based on YOLOv8 and PPF. By integrating 2D image detection and 3D point cloud processing, it can quickly and accurately recognize and locate charging guns in complex environments. Then, according to the application characteristics of unstructured scenarios, an integrated system of the wheeled mobile manipulator is designed and verified through simulation. Finally, based on the custom-developed wheeled mobile manipulator platform, plug-in autonomous charging experiments are carried out in unstructured scenarios. The results show that in three typical experimental scenarios, namely normal illumination without occlusion, dim light with slight occlusion, and dark with large-area occlusion, the method proposed in this paper can achieve rapid recognition and positioning of the charging gun, meeting the precision requirements that the angle error of the charging gun pose estimation does not exceed