# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 2 Related Works
The method was validated through simulation and demonstrated effective tracking and grasping of moving targets. [PERSON] et al. [24] proposed an image-based underactuated aerial manipulator control scheme with an image space visual servoing controller on a dynamic horizontal plane. Moreover, it decoupled the attitude tracking task from visual impedance control to address the underactuated nature of the system. [PERSON] et al. [25] described a system with dual manipulator arms with an RGB-D camera. The position information of the target object was obtained using artificial neural networks, and the grasping position and pose of the aerial manipulator were estimated by establishing a 3D model of the target object. [PERSON] et al. [26] proposed a guidance method based on IBVS comprising a passive adaptive controller based on position and velocity control to achieve autonomous grasping and transportation of target objects. [PERSON] et al. [27] introduced a vision-guided control method utilizing an image-based cylinder detection algorithm to identify elliptical surfaces and perform aerial operations. [PERSON] et al. [28] used stereo-depth perception to extract the target's position in the surrounding scene, obtaining the relative pose of the aerial manipulator and target object. While this research indicates that the aerial manipulator system has become a research hotspot in robotics, the results are relatively singular. Indeed, the control and positioningnavigation of the aerial manipulator system continue to face challenges when interacting with the environment. Regarding the structure of the aerial manipulator, numerous designs for the flight mechanical arm platform have been proposed for specific application scenarios [9; 10; 11; 12; 13; 14]. However, these are limited by complex structures, low control accuracy, and insufficient robustness. Therefore, developing a flexible and highly expandable aerial manipulator structure is the main focus of this study. Moreover, the aerial manipulator control algorithms proposed by previous studies require relatively complex kinematic and dynamic modeling, most of which are in the simulation stage and are challenging to apply in actual systems. Hence, real-machine flight has yet to be achieved [12; 14; 15; 19; 21; 23]. Therefore, this paper focuses on developing an experimental platform for a UAV with a manipulator with lower-level control, positioning and navigation, and top-level application algorithms, ultimately achieving real-machine flight of target object grasping. In addition, regarding the navigation and positioning strategy of the aerial manipulator, most apply the motion capture system (Vicon) [26; 27; 28]. Although the Vicon system can significantly improve the positioning accuracy of flying robots, its high price limits the aerial manipulator's application. Accordingly, this paper proposes an effective aerial manipulator control method and positioning navigation method in an indoor environment without a Vicon system. Innovatively, a vision-guided algorithm is proposed to perceive the target's position information in real time and adjust the attitude of the aerial manipulator. This study will guide other researchers in constructing experimental platforms and autonomous control of flight mechanical arms in the future.