# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 5 Conclusions  
The large number of parts in a complex product makes the repetition of path planning inevitable, which leads to low assembly efficiency. To solve this problem, this paper proposes a rapid APP method by reusing the prior path based on the RRT * algorithm.  
First, an assembly path is divided into three phases to reduce the difficulty of reuse, and the logistics phase of the path is taken as the main object of reuse. Second, the path planning of a part in the assembly space is converted to searching for a non-interference path in the configuration space. Third, a dual-tree fusion strategy and the optimal path selection strategy are designed to implement the PPR algorithm. Finally, the PPR algorithm and the RRT * algorithm are compared by example analysis.  
The examples and comparison list the comparison of results with different numbers of sampling points and ones with of RRT * and PPR, which conclude the advantage of the proposed method as follows: (1) The results with different numbers of sampling points show that the PPR algorithm is completed when the number of sampling points is small,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **Minimum Number of** \\ **Sampling Points** \\ \end{tabular} &
\begin{tabular}{c} **Total Running Time (s)** \\ \end{tabular} \\ \hline RRT * & 12,000 & 137.56 \\ PPR & 2500 & 24.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the planning results obtained by the RRT * and PPR algorithms.  
Figure 19: Success rate of the PPR algorithm and the RRT * algorithm of Case Study 1.  
which means the proposed method performs well in the case of small sampling sizes; (2) the results of RRT * and PPR show that if the maximum number of sampling points is small, the success rate of path planning by using the PPR algorithm is much greater than that of the RRT * algorithm; (3) the results of path planning with one and multiple start points show that the planning time and the number of sampling points of the PPR algorithm are much smaller than those of the RRT * algorithm, which means that the proposed method performs much better than the RRT * algorithm.  
Future work will focus on the application of the PPR algorithm on a dynamic environment and the situation which is the prior path is interrupted by moving obstacles. The improvement of the algorithm efficiency is a next research direction. What is more, the method in this paper has been decided to be conducted in other realms, and more applications will be given in the future research.  
Methodology, G.Y. and Y.C.; investigation, G.Y. and H.H.; software and validation, C.Z. and H.H.H.; writing--original draft preparation, C.Z. and H.H.H.; writing--review and editing, G.Y. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2018 YFB1701300, the National Natural Science Foundation of China, grant number 51875515, the Key Research and Development Program of Zhejiang Province, grant number 2020C0409, and the Science and Technology Project of Beijing, grant number Z191100001419014.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.