## 4 Experimentation
### Robot Grasping Experiment  
#### 4.3.1 Experiments in a Normal Environment  
In order to verify the application feasibility of the algorithm in robot grasping, an experimental environment in CoppeliaSim is presented, as shown in Figure 10. It includes a UR5 robotic arm, RG2 gripper, conveyor belt and vision sensor. The objects used in the experiment are all the same ordinary cuboid. The cuboid is a rigid body whose size is 20 \(\times\) 8 \(\times\) 8 cm. The manipulator can track until the grabbed object leaves the conveyor belt and it is deemed successful. \begin{table}
\begin{tabular}{c c c c} \hline  & **OPE Average** & **TRE Average** & **SRE Average** \\ \hline PF & 0.540 & 0.447 & 0.494 \\ \hline GPF & 0.618 & 0.556 & 0.530 \\ \hline IGPF & 0.690 & 0.645 & 0.617 \\ \hline Improvement rate & 11.65\% & 16.01\% & 16.42\% \\ \hline \end{tabular}
\end{table} 
Table 1: Initial parameter setting of algorithm. \begin{table}
\begin{tabular}{c c c c} \hline  & **OPE Average** & **TRE Average** & **SRE Average** \\ \hline PF & 0.561 & 0.501 & 0.565 \\ \hline GPF & 0.664 & 0.613 & 0.638 \\ \hline IGPF & 0.746 & 0.724 & 0.767 \\ \hline \end{tabular}
\end{table} 
Table 2: OS comparison of the three algorithms. Figure 9: DP SRE curve. Firstly, the conveyor belt is in a state of uniform linear motion. The target starts to move from the starting point of the conveyor belt. The repeated experiments are carried out at different speeds of the conveyor belt. The initial state of each experiment is the same. The continuous tracking image in the experiment is shown in Figure 11. The crawling success rate and average tracking time at different speeds are shown in Table 3.  
According to Table 3, the algorithm has a higher success rate at 0.1 m/s, and after exceeding 0.15 m/s, the success rate drops more. As the speed increases, the tracking distance of the robotic arm increases. The experimental platform in the real environment is similar to the simulation environment. It consists of UR3 robots, conveyor belts and cameras. Figure 12 shows the images continuously captured by the camera in the mobile production line tracking and grabbing experiment. The camera detects the target and the end pose of the robot starts to move. The pose of the target approaches, and the robotic arm tracks the target for synchronous follow-up until it meets the grasping conditions to achieve grasping. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Velocity (m/s)**} & **Number of** & \multirow{2}{*}{**Success Rate (\%)**} & **Average Tracking** \\  & **Experiments** & & **Time (s)** \\ \hline
0.1 & 50 & 88 & 1.2 \\ \hline
0.15 & 50 & 82 & 2.5 \\ \hline
0.25 & 50 & 76 & 3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tracking and Grasping results of uniform linear moving target. Figure 11: Tracking image. Figure 10: Simulation experiment scene. Similar to the simulation experiment, the real environment experiment also sets different conveyor speeds to repeat the experiment. Each experiment ensures that the initial pose of the robotic arm is the same. The initial pose of the target is the same. The conveyor speed is set to 0.1 m/s, 0.15 m/s and 0.25 m/s, respectively. The 50 grasping experiments are carried out, respectively. The statistical experiment grasping success rate and average tracking time is proposed. The experimental results are shown in Table 4.  
Then, the experimental data were further analyzed. The Euclidean distance between the target and the robot at the same time is calculated in the base coordinate system as the error, which is the ordinate of the curve. The horizontal axis represents the experimental time for the error to converge below the threshold. The error curve at different speeds is proposed. In the experiment, the minimum velocity of 0.1 m/s and the maximum velocity of 0.25 m/s draw error curves, respectively, as shown in Figure 13. Both thresholds are 0.1. As shown in the figures, the rate of change of the two-speed error curves shows a trend from large to small to large.