使用配备机载感知的软体无人机进行激进空中抓取
Aggressive Aerial Grasping using a Soft Drone with Onboard Perception
AI总结：
本文提出首个配备完整机载感知的软体空中操作器，通过被动闭合腱驱动软体抓手、神经网络关键点检测与鲁棒3D姿态估计相结合，实现室内外环境下最高2.0 m/s的视觉抓取及对移动目标的抓取。
摘要：
与猛禽展现出的惊人能力相反，飞行机器人在空中操作和抓取方面仍缺乏通用性和敏捷性。使用刚性机械臂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，这限制了高速下的性能。少数已报道的激进空中抓取示例要么依赖运动捕捉系统，要么无法在不同环境和抓取目标之间泛化。我们描述了首个配备完整机载感知管线的软体空中操作器，能够鲁棒地定位和抓取视觉与形态各异的物体。所提出的系统具有一种新型的被动闭合腱驱动软体抓手，能够在抓取时快速闭合，同时补偿位置误差、顺应目标物体形态并抑制反作用力。该系统包括一个机载感知管线，将基于神经网络的关键点检测器与最先进的鲁棒3D物体姿态估计器相结合，其估计结果进一步通过固定滞后平滑器进行优化。得到的姿态估计被传递给最小snap轨迹规划器，并由自适应控制器跟踪，该控制器完全补偿被抓取物体的附加质量。最后，基于有限元的控制器确定用于抓取的最优抓手配置。严格的实验证实，我们的方法能够实现动态、激进和通用的抓取。我们展示了在室内和室外环境中对多种物体进行完全机载视觉抓取，速度最高可达2.0 m/s——这是文献中报道的最快视觉抓取。最后，我们通过展示基于运动捕捉的对移动速度最高为0.3 m/s、相对速度最高为1.5 m/s的目标的抓取，在将我们平台的实用性扩展到静止目标之外方面迈出了重要一步。
英文摘要：
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 aggressive 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 closing 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 with a state-of-the-art robust 3D object pose estimator, whose estimate is further refined using a fixed-lag smoother. 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. Rigorous experiments confirm that our approach enables dynamic, aggressive, and versatile grasping. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
