# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 1 Introduction  
With the development of indoor positioning and navigation systems and intelligent control technologies, rotary-wing unmanned aerial vehicles (UAVs) have gained increasing attention in various fields. Their simple structure, agile movement, and ability to hover in place make them widely applicable in indoor search, disaster site patrols, and fire extinguishing [1; 2; 3]. In these applications, the rotary-wing UAV is typically used as an environmental perception and motion platform equipped with sensors. During its flight, it does not contact the external environment, lacking the ability to interact with it actively [4]. By equipping UAVs with an aerial manipulator, effectively forming an aerial manipulator system, their active operational capabilities can be significantly enhanced, enabling them to perform tasks such as sample collection in extreme environments and transfer and delivery of hazardous materials, increasing the interaction capability between rotary-wing UAVs and the environment [5]. Given that these aerial manipulator systems can perform operations in the air with high precision, they have emerged as a new research focus in robotics [6]. Typically, the aerial manipulator comprises a rotary-wing UAV and a multi-degree-of-freedom manipulator. The rotary-wing UAV possesses high dimensionality, underactuation, and nonlinearity. When operating with a multi-degree-of-freedom manipulator, the overall dimensionality of the aerial manipulator system increases significantly, greatly expanding the application range of rotary-wing UAVs and providing broad prospects and societal implications [7]. However, the manipulator's accuracy of UAV positioning and target grasping currently requires improvement. To address this challenge, research has achieved the fast grasping capability of an aerial manipulator using a motion capture system (Vicon, Hauppauge, NY, USA) for UAV localization [8]. However, achieving autonomous grasping relying solely on the UAV's perception is yet to be fully realized. Especially in practical applications, the aerial operation of the manipulator is prone to losing target information, making it challenging to reconstruct three-dimensional spatial information and estimate real-time depth. Therefore, this paper analyzes the latest research and development trends in aerial manipulators and robot grasping. It focuses on key technologies during the grasping process of an aerial manipulator without the motion capture system (Vicon) in indoor environments. We propose a vision-guided autonomous positioning and control system for the aerial manipulator. The paper designs a hierarchical control system for the aerial manipulator's pose and utilizes visual guidance for feature extraction and grasping pose estimation. Considering the range of motion during target grasping and the requirement to maintain a safe distance from the ground, a laser sensor is introduced to measure the distance between the aerial robot and the target object. An iterative algorithm is applied for target searching. Finally, the autonomous grasping functionality of lightweight cylindrical objects is achieved. The main contributions of our study are as follows:  
1. We propose an integrated platform for a single-degree-of-freedom aerial manipulator that is low-cost, structurally compact, and easy to implement in hardware. 2. We design a hierarchical control system for an aerial manipulator with a flight control system for UAV attitude, position control achieved via Pixhawk4's inner and outer loop PID algorithms, and an aerial manipulator with end-effector control controlled by Jetson Xavier NX. 3. We propose a visual guidance algorithm for the real-time perception of target object position information by fusing a depth camera with laser-ranging data. Ultimately, a segmented grasping strategy is employed to achieve autonomous object retrieval. This system can effectively address target loss, 3D spatial information reconstruction, and real-time depth estimation during aerial operations. 4.