# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 6 Visual Adaption Results  
The purpose of the vision adaptive feature is to grasp target objects adaptively according to their shape and motion. To achieve this, the experimental setup was divided into three steps. The first step was to determine the shape and orientation of the target object. The second step was to find the positional information, which was converted from image pixels to real-world coordinates so that the error of the conversion at an unknown target object height can be determined. In the third step, the speed of the target was obtained, and the manipulator was set to chase the target at a constant speed. The performance of the visual adaptivity is listed in Table 3 for three different target objects, namely, square, circle, and rectangle objects, respectively. The maximum standard deviation of measurements in sensing an object's height was approximately 4 mm, which is in reasonable range limited by a commercially available depth camera. For the Intel Realsense camera used in this study, its depth accuracy is 2.5 mm to 5 mm at 1 m distance from object to camera, which reconfirms the mentioned 4 mm standard deviation is engineering reasonable. Certainly, should a better depth camera be used, this number can be further improved. Once a target's height was measured, the smart gripper started to locate the target and follow its movement using its eye-in-hand visual servoing function. Table 4 shows results of the success rate for the gripper to perform the visual servoing function at different \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Height** & **Average Height of** & **Average Height of** & **Standard** & **Error Rate** \\
**Measurement** & **Calculation (m)** & **Measurement (m)** & **Deviation (m)** & **(\%)** \\ \hline Square & 0.049 & 0.050 & 0.004 & 1.71\% \\ \hline Circle & 0.053 & 0.051 & 0.001 & 5.16\% \\ \hline Rectangle & 0.024 & 0.024 & 0.002 & 1.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Height measurement of target objects. speeds of the conveyor. The results show the maximum conveyor speed that can be chased up by the gripper was approximately 70 mm/s. It was also observed that success rates when speed was below 70 mm/s remained at 100% for the different targets and rapidly decreased at 70 mm/s. Constrained by the total length of the conveyor belt being 70 cm and its effective length being 50 cm to prevent objects dropping at its two edges, the maximum speed that can be tested was then limited. Figure 27 shows the real-time velocity of the manipulator's tool center point (TCP) when the gripper attempts to grasp a moving object. The velocity profile was divided into four regions. In region I, the gripper calculates the moving speed and shape of the target. In Region II, the gripper sends the target information to the controller to track the target. In region III, the gripper speed was set identical to that of the moving target. Subsequently, the gripper's TCP center starts to align with the moving target's center. During the finding and tracking process, the gripper started to change the angle between its fingers to ensure a proper grasping posture. Finally, in region IV, the robotic arm prepares to execute a grasp mission while waiting for its speed to be identical to that of the target. As shown in Figure 27, the distance moved by the object can be obtained by multiplying the speed of the conveyor with time. The distance that the manipulator moves is determined by the manipulator's feedback. When the distance between the robot and the object is close enough to grasp, and when the object is directly below the eye-in-hand gripper module, the object can be grasped by the robotic gripper.