High-speed aerial grasping using a soft drone with onboard perception

Samuel Ubellacker, Aaron Ray, James M. Bern, Jared Strader, Luca Carlone
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Abstract

Contrary to the stunning feats observed in birds of prey, aerial manipulation and grasping with flying robots still lack versatility and agility. Conventional approaches using rigid manipulators require precise positioning and are subject to large reaction forces at grasp, which limit performance at high speeds. The few reported examples of high-speed aerial grasping rely on motion capture systems, or fail to generalize across environments and grasp targets. We describe the first example of a soft aerial manipulator equipped with a fully onboard perception pipeline, capable of robustly localizing and grasping visually and morphologically varied objects. The proposed system features a novel passively closed tendon-actuated soft gripper that enables fast closure at grasp, while compensating for position errors, complying to the target-object morphology, and dampening reaction forces. The system includes an onboard perception pipeline that combines a neural-network-based semantic keypoint detector, a state-of-the-art robust 3D object pose estimator, and a fixed-lag smoother to estimate the pose of known objects. The resulting pose estimate is passed to a minimum-snap trajectory planner, tracked by an adaptive controller that fully compensates for the added mass of the grasped object. Finally, a finite-element-based controller determines optimal gripper configurations for grasping. Experiments on three different targets confirm that our approach enables dynamic, high-speed, and versatile grasping, all of which are necessary capabilities for tasks such as rapid package delivery or emergency relief. We demonstrate fully onboard vision-based grasps of a variety of objects, in both indoor and outdoor environments, and up to speeds of 2.0 m/s—the fastest vision-based grasp reported in the literature. Finally, we take a major step in expanding the utility of our platform beyond stationary targets, by demonstrating motion-capture-based grasps of targets moving up to 0.3 m/s, with relative speeds up to 1.5 m/s.

Abstract Image

使用带有机载感知功能的软式无人机进行高速空中抓取
与在鸟类身上观察到的惊人壮举相反,飞行机器人的空中操纵和抓取仍然缺乏多功能性和灵活性。使用刚性机械手的传统方法需要精确定位,并且在抓取时会受到较大的反作用力,从而限制了高速飞行时的性能。已报道的少数高速空中抓取实例依赖于运动捕捉系统,或者无法在不同环境和抓取目标之间通用。我们介绍了首个配备全板载感知流水线的软性空中机械手实例,该机械手能够稳健地定位和抓取视觉和形态各异的物体。该系统采用了新型被动闭合式肌腱驱动软抓手,可在抓取时快速闭合,同时补偿位置误差、适应目标物体形态并抑制反作用力。该系统包括一个板载感知管道,它结合了基于神经网络的语义关键点检测器、最先进的鲁棒三维物体姿态估计器和固定滞后平滑器来估计已知物体的姿态。由此产生的姿态估计值被传递给最小捕捉轨迹规划器,并由自适应控制器进行跟踪,该控制器可对所抓物体的附加质量进行完全补偿。最后,基于有限元的控制器将确定最佳的抓取配置。在三个不同目标上进行的实验证实,我们的方法能够实现动态、高速和多功能抓取,所有这些都是快速包裹递送或紧急救援等任务所必需的能力。我们展示了在室内和室外环境中完全基于视觉的各种物体抓取,速度高达 2.0 米/秒--这是文献中报道的最快的基于视觉的抓取速度。最后,我们展示了基于运动捕捉技术的抓取,抓取速度可达 0.3 米/秒,相对速度可达 1.5 米/秒,从而在扩展我们平台的实用性方面迈出了重要一步,使其超越了静止目标。
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