# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 6 Experimental Validation and Analysis
### Comparative Evaluation of the Performance of the Improved Full-Coverage Path Planning Method  
The resistance between the robot and the water surface was an essential parameter in the experimental evaluation. Due to the complexity of calculating the resistance formula, this study aimed to obtain the resistance value through experimentation. First, a force sensor was installed on the robot body, propelled forward at a constant velocity of \(v=1\ \mathrm{m/s}\). During this process, the force sensor experienced a corresponding force, converted into an electrical signal output. Subsequently, the output data of the force sensor were recorded and analyzed, resulting in a measured resistance between the robot and the water surface of approximately \(f=65\ \mathrm{N}\).  
The parameters for the robot were set as follows: the resistance between the water surface and the robot was approximately \(f=65\ \mathrm{N}\), the constant velocity of the robot along the water surface was \(v=1\ \mathrm{m/s}\) the power of the Raspberry Pi and circuit board was \(8\ \mathrm{W}\), the power of the laser radar was \(3\ \mathrm{W}\), and the power of the depth camera was \(3.5\ \mathrm{W}\). Therefore, the power of the robot control module was \(Pc=8\ \mathrm{W}+3\ \mathrm{W}+3.5\ \mathrm{W}=14.5\ \mathrm{W}\). The turning time of the robot was \(t_{t}=2\ \mathrm{s}\), and the turning power was \(P_{t}=40\ \mathrm{W}\). The mass of the robot was \(m=20\ \mathrm{kg}\). Substituting these parameters into the energy consumption model for the robot served as the performance evaluation objective function, guiding intelligent algorithms in achieving the goal of full-coverage tasks with low energy consumption.  
The improved full-coverage path planning method proposed in this paper was compared with the original approach by conducting empirical comparative robot experiments. Ten repetitions of experiments were performed for each method. The objective was to observe the performance metrics of water surface robots when using different algorithms, including motion time, hover time, total time, path length, coverage rate, energy consumption, etc. The average values of these metrics were calculated to assess the performance and effectiveness of the new algorithm.  
Observing the statistical results and analysis of the two algorithms' metrics in Table 2, significant differences in specific performance indicators emerged in full-coverage path planning. Concerning the time consumption, the improved full-coverage algorithm under digital twin collaboration, compared to the original approach, showed little difference in motion time but exhibited substantial reductions in hover time and total time, with a decrease of approximately 83%. Regarding coverage rate, both algorithms achieved complete coverage, with minimal differences in path length.  
Notably, regarding energy consumption, the improved algorithm demonstrated a reduction of approximately 16.9%. This decrease in energy consumption was crucial for reducing the overall weight of the water surface cleaning robot and ensuring the smooth completion of the designated area traversal tasks. The optimization of motion and energy consumption metrics played a vital role in enhancing the overall performance of the robot system, providing a significant advantage for its practical applications.