This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors namely a LiDAR, cameras and IMUs are utilized. For processing of the acquired sensory data, pose estimation pipelines are devised for industrial objects of both known and unknown geometries. We further propose an active learning pipeline in order to increase the sample efficiency of a pipeline component that relies on Deep Neural Networks (DNNs) based object detection. All these algorithms jointly address various challenges encountered during the execution of perception tasks in industrial scenarios. In the experiments, exhaustive ablation studies are provided to validate the proposed pipelines. Methodologically, these results commonly suggest how an awareness of the algorithms' own failures and uncertainty ("introspection") can be used tackle the encountered problems. Moreover, outdoor experiments are conducted to evaluate the effectiveness of the overall system in enhancing aerial manipulation capabilities. In particular, with flight campaigns over days and nights, from spring to winter, and with different users and locations, we demonstrate over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As a result, we show the viability of the proposed system in future industrial applications.
翻译:文章展示了一种新型的远程存在系统,用于推进动态和无结构环境中的空中操纵。 拟议的系统不仅具有一个机智装置,而且有一个虚拟现实(VR)界面,提供机器人工作空间的实时3D显示器,以及对其远程定位操作员的便利指导。 为此,使用了多种传感器,即LIDAR、照相机和IMUs。 用于处理获得的感官数据,为已知和未知的地貌的工业物体设计了估计管道。 我们进一步提议建立一个积极的学习管道,以提高依赖深神经网络(DNNS)进行天体探测的管道部件的样本效率。 所有这些算法共同应对在工业情景中执行感知任务期间遇到的各种挑战。 在实验中,提供了详尽的模拟研究,以验证拟议的管道。 从方法上看,这些结果通常表明如何使用算法本身的失败和不确定性(“渗透” ) 来解决所遇到的问题。 此外,还进行了室外实验,以评估基于深层神经网络(DNNNNW)的管道部件的试样效率, 以及从整个系统在加强空中操纵的冬季运动中, 展示了整个系统在空中操作的能力。