In this work, we present a pragmatic approach to enable unmanned aerial vehicle (UAVs) to autonomously perform highly complicated tasks of object pick and place. This paper is largely inspired by challenge-2 of MBZIRC 2020 and is primarily focused on the task of assembling large 3D structures in outdoors and GPS-denied environments. Primary contributions of this system are: (i) a novel computationally efficient deep learning based unified multi-task visual perception system for target localization, part segmentation, and tracking, (ii) a novel deep learning based grasp state estimation, (iii) a retracting electromagnetic gripper design, (iv) a remote computing approach which exploits state-of-the-art MIMO based high speed (5000Mb/s) wireless links to allow the UAVs to execute compute intensive tasks on remote high end compute servers, and (v) system integration in which several system components are weaved together in order to develop an optimized software stack. We use DJI Matrice-600 Pro, a hex-rotor UAV and interface it with the custom designed gripper. Our framework is deployed on the specified UAV in order to report the performance analysis of the individual modules. Apart from the manipulation system, we also highlight several hidden challenges associated with the UAVs in this context.
翻译:在这项工作中,我们提出了一个务实的方法,使无人驾驶飞行器(无人驾驶飞行器)能够自主地执行高度复杂的物体选取和位置任务,本文件主要受2020年MBZIRC挑战-2的启发,主要侧重于在户外和全球定位系统封闭环境中组装大型三维结构的任务,该系统的主要贡献是:(一) 一种基于目标定位、部分分割和跟踪的基于计算效率的、基于计算效率的、基于统一多任务视觉感知的新颖的深层次学习系统,(二) 一种基于掌握状态的新颖的深入学习估算,(三) 一种收回式电磁控制器设计,(四) 一种利用基于高速(5000Mb/s)的高速度的高级MIMO的远程计算方法,使无人驾驶飞行器能够在远程高端计算服务器上执行精密的密集任务,以及(五) 系统集成系统,将几个系统组件编织在一起,以开发一个优化的软件堆。我们使用DJI Matrie-600 Pro,一种Hex-rotor UAV, 和它与定制的定制控制器的定制的定制连接系统接口,我们的框架也用于与UAV的单个操作的单个操作。