# Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition
## 6 Experiments  
In order to further verify the effectiveness in the actual hoisting work of the control scheme proposed in this paper, we conduct the experimental test using the four-degrees-of-freedom hoisting platform in the laboratory. In the experiment, we drive the heavy-long load component to the target position, and the weight of the load component is up to 300 kg, the overall dimensions of the load component are 0.4 \(\times\) 0.4 \(\times\) 1.2 (m) of height, width, and length. For the hoisting platform, the vibration of load and equipment is inevitable when it is hoisting heavy-long load components, which is also consistent with the disturbance condition in the actual hoisting operation. OMRON encoders (E6B2-CWZ6C) connected to the motor are used to measure the velocities. The images are captured by Mako camera (AVT, Germany), using GigE Vision interface and Power-over-Ethernet. Through  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Convergence Time (s)** & **Computational Time (ms)** & **Number of Servo Cycles** \\ \hline ASMCN visual servo (ours) & 18.05 & 150 & 120 \\ PC visual servo & 22.20 & 149 & 149 \\ KF visual servo & 20.21 & 157 & 129 \\ PSMC visual servo & 22.15 & 155 & 143 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison result.  
Figure 8: Comparison result of accumulated visual errors.  
wired network transmission, we can realize the real-time transmission of visual system and control system. The experimental platform is shown in Figure 9. The target is composed of four black origins and a rectangular frame. The Laplacian of the Gaussian algorithm is used to extract the line feature and inner region feature in the image. In the actual condition, the change of light intensity will affect the image quality, and then affect the accuracy of image feature extraction. Therefore, we use adaptive histogram equalization [37] to preprocess the image. Moreover, the feature extraction results of the convergence process are shown in Figure 10.  
We carried out two positioning experiments (A and B) by changing the initial position. Firstly, the load component is moved to the target position by the manual system to obtain the desired image and the camera records the characteristic images of different positions. Then, we return to component the initial position, start the automatic control system, and drive the load component to the target position by the proposed scheme. For the IBVS system proposed in this paper, its convergence region is limited due to the nonlinearity and singularity of the desired image to the driving frame. The extracted target points will match to the adjacent target points in the desired image.  
Figure 11a,b depict the visual errors of experiment A, from which we can see that, although the visual error value of the initial position is large, the curves converge quickly and finally reach zero. Obviously, the visual error curve has a zigzag character, which is caused by the disturbance. However, the velocity curve in Figure 12 is relatively smooth, which indicates that the delay of mechanical system is lower and the encoder sensitivity is higher. This also shows that the algorithm in this paper can output stable control signals by adjusting the adaptive gain when the visual signal has a small vibration. As for experiment B, by comparing the (e) and (f) in Figure 13, the visual error of rotation converges earlier than that of the translational direction. The velocity curves in Figure 14 have almost no oscillation effect and can gradually approach zero, which indicates that our method has a robust performance on disturbance suppression and precise positioning.  
Figure 10: The feature extraction results of the convergence process.  
Figure 9: Experimental platform.