# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 4 Experimental Results
### Discussion  
We also use the simulation environment to implement three path-planning algorithms to compare with the algorithms proposed in this paper:  
1. A star: Using the intelligent backtracking mechanism of greedy A star (GA) search to pass through a given garbage area.
2. CPP: Use precise cell decomposition methods to decompose free space into simple, non-overlapping regions. Enable robots to easily sweep through these unobstructed areas.
3. KNN: Path planning is accomplished using the locally optimal KNN algorithm.  
As shown in Table 5, the A star algorithm does not have the ability to recognize the target region; we turn the garbage region into the corresponding path point, and the result is that the performance of the A star algorithm is still unsatisfactory. In the CPP algorithm, due to its mechanical nature and blind obedience rows, the planning path is too long although the planning time and coverage are very excellent. In contrast to the KNN algorithm, the BF-CPP + SA-based sweeping path accuracy method performs well in terms of path length and garbage area coverage, although the planning time is slightly inferior.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Time/s** & **Length/m** & **Coverage/\%** \\ \hline A star & 6.1 & 16.9 & 50.8 \\ CPP & 10.8 & 31.1 & 97.0 \\ BF-CPP + SA & 20.5 & 22.7 & 96.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance of algorithms under the scenario of equalization of road garbage distribution.  
Figure 16: Scenarios with varying sparsity. Where blue triangles are obstacles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Time/s** & **Length/m** & **Coverage/\%** \\ \hline A star & 6.4 & 15.4 & 70.9 \\ CPP & 10.3 & 30.3 & 97.3 \\ BF-CPP + SA & 16.7 & 20.4 & 95.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of algorithms’ performance in scenarios with aggregated but overall sparse road debris.  
The specific performance of each algorithm is shown in Figure 17. The line graph reflects the cleaning efficiency of each algorithm, and the two subplots are the planning time and the path length obtained from the planning, respectively. The floating points on the subplots indicate the average value taken after multiple experiments.  
In summary, the exact cleaning path method based on BF-CPP + SA proposed in this article can comprehensively solve the mechanical and aimless nature of CPP while maintaining the completion of cleaning. Compared with traditional path planning algorithms, it can also better solve the problem of traditional algorithms being unable to correctly identify garbage and obstacles. Compared to the A star algorithm and the CPP algorithm, there is also a significant performance improvement.  
Through theoretical analysis and experimental study, we demonstrate the performance and capability of the proposed BF-CPP + SA method from various aspects. In the experiments, traditional algorithms such as the A star algorithm and KNN algorithm have less than optimal sweeping efficiency because they do not have the ability to identify the target area. The CPP algorithm is able to traverse the garbage area, but its path length and inefficiency are discouraging. In particular, the exact method based on BF-CPP + SA can guarantee an optimal solution for TSP-CPP, but due to the more complex processing flow, it is able to produce high-quality tours but still needs to be optimized in terms of planning speed.  
Figure 17: Performance of BF-CPP + SA algorithm compared with traditional path planning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Time/s** & **Length/m** & **Coverage/\%** \\ \hline A star & 4.9 & 51.1 & 48.3 \\ CPP & 4.7 & 92.4 & 98.1 \\ KNN & 5.1 & 62.6 & 65.6 \\ BF-CPP + SA & 7.6 & 60.5 & 96.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of BF-CPP + SA algorithm compared with traditional path planning.