# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 3 Hardware Structure and Control Method of Aerial Manipulator System
### Implementation of the Aerial Manipulator System  
The control system proposed for the aerial manipulator adopts a hierarchical control approach comprising the autonomous navigation system for quad-rotor UAVs and the vision-guided manipulator operation control system. The autonomous navigation system for quad-rotor UAVs includes real-time positioning and attitude control via LiDAR. The vision-guided manipulator operation control system extracts target object feature information and controls the arm for grasping tasks. The entire control system for the aerial manipulator is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Information** & **Parameter** \\ \hline Body size & 450 mm \\ Motor model & AIR 2216/KV880 \\ Electronic speed controller & XRotor Lotte 20A \\ Propeller model & 10 inch nylon blades \\ Battery & Grignard 4S1P5300 mAh \\ Servo motor model & HWZ020 270 degrees \\ The length of manipulator & 17 mm \\ Weight & 2016 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the aerial manipulator system.  
Figure 3: The depth camera D435 and the laser sensor No. 2 TFmini.  
The implementation process is as follows:  
First, the UAV takes off and uses the LiDAR-based SLAM to construct surrounding map information, generating a target trajectory. Then, the trajectory is determined by the attitude settlement module, obtaining the desired position \(P_{\rm d}=[{\rm x}_{\rm d},y_{\rm d},z_{\rm d}]\) and the desired attitude angle \(\varphi_{\rm d}\). These are outputted to the position controller. The actual position of the UAV can be obtained through sensors, assumed to be \(P=[{\rm x},{\rm y},{\rm z}]^{T}\). Utilizing the PID control algorithm, a linear relationship is established between the error quantity of the desired position and the actual position, which is represented as follows:  
\[\begin{split}\ddot{x}&=kP(xd-x)+kI\!\int{(xd-x)dt+kD( \dot{x}d-\dot{x})}\\ \ddot{y}&=kP(yd-y)+kI\!\int{(yd-y)dt+kD(\dot{y}d-\dot {y})}\\ \ddot{z}&=kP(zd-z)+kI\!\int{(zd-z)dt+kD(\dot{z}d- \dot{z})}\end{split} \tag{1}\]  
where \(k_{p}\), \(k_{L}\), and \(k_{D}\) represent the proportional, integral, and derivative coefficients, respectively; \(\dot{x}\) represents the first-order derivative of \(\dot{x}\); and \(\ddot{x}\) represents the second-order derivative of \(\dot{x}\).  
Second, based on Equation (1), the position controller calculates the UAV's total lift, desired roll angle \(\varphi_{\rm d}\), and desired pitch angle \(\theta_{\rm d}\) through the dynamic equations [30]. The actual roll angle \(\phi\) and pitch angle \(\theta\), obtained from the sensors, are input into the attitude controller module. Using PD control and proceeding through a dynamic allocation model, the motor rotation speed \(w_{i}(i=1,2,3,4)\) is decoupled.  
Third, when the UAV enters the operational range, it utilizes the depth camera D435 to search for the target object. The laser sensor No. 2 TFmini, obtains the height information between the UAV and target object, enabling the extraction of three-dimensional feature information of the target object. Finally, the control system outputs the angles of various joint servo motors, denoted as \(\theta_{i}(i=1,2)\), to the servo motor controller for controlling the grasping pose, which controls the manipulator to complete the grasping task.