## 5 Experimental Research  
The cleaning trajectory of the photovoltaic panel cleaning robot arm was studied experimentally to investigate the reliability of the optimal trajectory planning based on the optimized sparrow algorithm combined with the seven-order non-uniform B-spline curve interpolation time. During the experiment, through the mapping relationship between the joint space and the drive space, the displacement variable of the hydraulic actuator was converted into the angle variable, and the angle sensor was used to realize the closed-loop control of the respective joint angle to improve the accuracy of the control. The sensor installation position of the respective joint of the boom is shown below.  
During the experimental process, the functions of the three joint angle variables over time after fitting the seven-degree non-uniform B-spline curve were used as input variables. The inclination angles of the respective joint detected by the sensors installed on the arm mechanism were fed back to the controller through the bus and compared with the input. The difference was then used as the input of the control system to control the action of the multi-way valve, achieving closed-loop control, thereby improving the accuracy of system control and avoiding damage to the photovoltaic panel surface caused by low precision control of the end cleaning device. The trajectory tracking curve and cleaning effect of the respective joint of the arm frame before and after optimization are shown in the following Figure 22.  
As depicted in Figures 23 and 24, during the autonomous cleaning operation of the photovoltaic panel cleaning robot, the respective joint angle ran smoothly and continuously during the trajectory tracking process with high curve overlap, thus confirming the effectiveness of the optimized sparrow algorithm combined with the time-optimal trajectory of the seven-degree non-uniform B-spline curve. During the cleaning process, there was no damage to the photovoltaic panel surface and the cleaning effect was good, which can achieve intelligent cleaning of the photovoltaic panel surface, increase work efficiency, and save labor costs.  
As depicted in Figure 25, by analyzing the trajectory tracking error curves before and after optimization, it can be seen that the angle errors generated during the trajectory tracking control process of each joint before optimization are \([-1.4^{\circ},1.2^{\circ}]\), \([-1.2^{\circ},1.1^{\circ}]\), \([-1.3^{\circ},1.2^{\circ}]\), and the trajectory tracking errors of each joint after optimization are \([-1.7^{\circ},1.3^{\circ}]\), \([-1.6^{\circ},1.3^{\circ}]\), and \([-1.5^{\circ},1.4^{\circ}]\), which are all within the allowable range. Although the optimized trajectory tracking error has increased, the trajectory running time has decreased by 18.7%, and there has been no safety warning during the cleaning process, which meets the requirements of actual cleaning conditions and proves the reliability of its trajectory control.  
Figure 22: Installation position of angle sensor on boom mechanism.  
Figure 23: Trajectory tracking of each joint of the cleaning arm before and after optimization. (**a**) Unoptimized big arm; (**b**) unoptimized lower arm; (**c**) unoptimized sweeping disk; (**d**) optimized big arm; (**e**) optimized lower arm; (**f**) optimized sweeping disk.  
Figure 24: Cleaning effect of photovoltaic panel cleaning robot.