Aerial transportation and manipulation have attracted increasing attention in the unmanned aerial vehicle field, and visual servoing methodology is widely used to achieve the autonomous aerial grasping of a target object. However, the existing marker-based solutions pose a challenge to the practical application of target grasping owing to the difficulty in attaching markers on targets. To address this problem, this study proposes a novel image-based visual servoing controller based on natural features instead of artificial markers. The natural features are extracted from the target images and further processed to provide servoing feature points. A six degree-of-freedom (6-DoF) aerial manipulator system is proposed with differential kinematics deduced to achieve aerial grasping. Furthermore, a controller is designed when the target object is outside a manipulator’s workspace by utilizing both the degrees-of-freedom of unmanned aerial vehicle and manipulator joints. Thereafter, a weight matrix is used as basis to develop a multi-tasking visual servoing framework to integrate the controllers inside and outside the manipulator’s workspace. Lastly, experimental results are provided to verify the effectiveness of the proposed approach.

中文翻译：

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基于自然特征的视觉操纵与空中操纵器的目标

空中运输和操纵在无人飞行器领域引起了越来越多的关注，并且视觉伺服方法被广泛用于实现对目标物体的自主空中抓取。然而，由于难以将标记物附着在目标物上，因此现有的基于标记物的解决方案对目标物抓取的实际应用提出了挑战。为了解决这个问题，本研究提出了一种基于自然特征而不是人工标记的基于图像的新型视觉伺服控制器。从目标图像中提取自然特征，并进行进一步处理以提供伺服特征点。提出了六自由度（6-DoF）空中操纵器系统，并推导了差分运动学以实现空中抓握。此外，当目标物体位于机械手工作空间之外时，可以利用无人机和机械手关节的自由度来设计控制器。此后，权重矩阵用作开发多任务视觉伺服框架的基础，以集成操纵器工作空间内外的控制器。最后，提供实验结果以验证所提出方法的有效性。