# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 2 Related Works  
Research on aerial manipulator systems focuses primarily on the system's structure, control system approach, and visual navigation strategy. For example, in the field of aerial manipulator system structure, [PERSON] et al. [9] used a 5-DOF aerial manipulator system to achieve the grasping of cylindrical objects. [PERSON] et al. [10] designed a humanoid dual-arm aerial manipulator for grasping operations. Meanwhile, considering the high cost and risk involved in large infrastructure inspections, [PERSON] et al. [11] utilized a rotary-wing UAV equipped with a compact single-DOF manipulator to perform facility inspections and sensor installation. [PERSON] et al. [12] researched a three-arm aerial manipulator using a flying robot for rope fixation and removal tasks and developed a manipulator structure suitable for complex operations. [PERSON] et al. [13] presented a novel aerial manipulator system with a self-locking universal joint mechanism. They also designed a dynamic gravity compensation mechanism and optimized the battery placement and gear ratio to minimize the weight imbalance during arm movements. Additionally, [PERSON] et al. [14] designed a novel aerial manipulator structure with a 5-DOF manipulator and batteries placed at the front and rear of a quad-rotor UAV, respectively. This design expanded the workspace and minimized the center of mass offset. Experimental results demonstrated the successful capture of targets on a moving platform. Regarding the aerial manipulator control system, [PERSON] et al. [15] proposed an aerial manipulator system with a low-dimensional simplified mode. The system was validated through simulated aerial flights in various target-tracking scenarios. Furthermore, [PERSON] et al. [16] addressed the issue related to team coordination for transporting payloads with aerial manipulator systems. A leader-follower setup was implemented to increase the system's autonomy, where each manipulator's gripper was equipped with force/torque sensors to enhance the UAV's flight stability. The proposed control method was validated through simulation in the Gazebo environment. Meanwhile, [PERSON] et al. [17] presented the system design, modeling, and control of a chain-based aerial manipulator. This system provided a hybrid modeling framework for modeling the system in free-flight and aerial manipulation modes. Moreover, [PERSON] et al. [18] proposed a constraint-oriented design optimization method for dual-arm aerial robots to increase the robot's workspace while reducing weight. In this configuration, the arms are separated from the aerial platform through long-distance linkages similar to pendulums, improving safety in human interaction scenarios. Due to the development of machine vision and the decreasing cost of visual sensors, the research on utilizing vision sensors to obtain target object position information to estimate grasping poses in aerial manipulators has gained widespread attention. [PERSON] et al. [19] proposed a machine vision servoing method based on natural features to control an aerial manipulator for autonomous aerial grasping of target objects. [PERSON] et al. [20] used an image-based visual servoing (IBVS) controller to maintain a simple gripper for autonomously grasping stationary objects. However, the depth estimation in the IBVS controller may be less accurate. Accordingly, [PERSON] et al. [21] adopted a UAV vision-based object detection method based on the YOLOv3 framework to identify target objects in complex environments using a gimbal camera. Meanwhile, [PERSON] et al. [22] designed a three-dimensional point cloud extraction method that combines object detection and background subtraction to improve the grasp evaluation strategy. The proposed method was experimentally validated to estimate effective grasping poses for aerial manipulators. [PERSON] et al. [23] presented an IBVS control strategy for tracking and grasping moving targets in UAV systems.