# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 7 Conclusions  
The traditional A* algorithm has some shortcomings in global path planning, such as low efficiency, many redundant points, and uneven path, etc., and it cannot realize the function of avoiding obstacles. From the point of view of path-planning efficiency and obstacle-avoidance function, this paper designed an optimization algorithm based on the fusion of improved A* and Open_Planner. Based on the global optimal path, dynamic obstacle avoidance was carried out to ensure that the target point can be reached efficiently and safely. In this paper, the selection of the nodes of the A* algorithm, the removal of unnecessary extension points and the improvement of node storage structure improve the computational efficiency of A* algorithm. The key points of the global planning are taken as the intermediate nodes of the local-planning algorithm, and, finally, the algorithm fusion was realized. The simulation results show that the efficiency of the A-OP algorithm is significantly improved, and the removal of redundant points in the path means the path tends to be smoother.  
The A-OP algorithm not only makes up for the defects of the A* algorithm, and completes the global path planning, but also avoids the obstacles in the path in time, and reaches the destination safely and efficiently. In subsequent research, the algorithm will  
Figure 16: Algorithm performance. (**a**) Planning time, (**b**) Path length.  
Figure 15: Obstacle avoidance experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental** & \multicolumn{2}{c}{**Dijkstra**} & \multicolumn{2}{c}{**Improved A* **} & \multicolumn{2}{c}{**Proposed Method (A-OP)**} \\
**Number** & & & & & & \\ \hline  & **Path** & **Time/s** & **Path** & **Time/s** & **Path** & **Time/s** \\  & **Length/m** & **Time/s** & **Length/m** & **Time/s** & **Length/m** & **Time/s** \\ \hline
1 & 11.45 & 6.37 & 10.13 & 4.22 & 9.71 & 1.37 \\
2 & 56.87 & 22.09 & 54.28 & 19.91 & 53.53 & 15.18 \\
3 & 33.15 & 14.99 & 31.43 & 13.22 & 30.93 & 9.91 \\
4 & 32.17 & 12.01 & 29.66 & 10.87 & 29.04 & 6.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results of the three algorithms.  
continue to be optimized to continuously improve the actual efficiency and application value of path planning.  
Conceptualization, Y.M. and P.P.; methodology, Q.S.; software, Y.M.; validation, Y.M.; formal analysis, Y.M.; investigation, P.P.; resources, P.P.; data curation, Q.S.; writing--original draft preparation, Q.S.; writing--review and editing, P.P.; visualization, Y.M.; supervision, Q.S.; project administration, Q.S.; funding acquisition, P.P. and Q.S. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (52202496, 61771265); Postgraduate Research & Practice Innovation Program of School of Information Science and Technology, Nantong University (NTUSISTPR21-010); the Natural Science Foundation for Colleges and Universities in Jiangsu Province (22 KJB520007) and the Science and Technology Foundation of Nantong (MS22021034, MS12022015).  
Not applicable.  
The authors declare no conflict of interest.