# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 2 Path Planning Algorithm
### Improvement of the A* Algorithm
#### 2.2.2 Optimization Using the SSA
(a) and (d) show the global view and local details of the original path; (b) and (e) display the globally smoothed path and its local section after B-spline interpolation; (c) and (f) demonstrate the panoramic and zoomed-in views of the path processed by the improved B-spline interpolation smoothing algorithm. For the path smoothing algorithms, the test results indicate that the Improved B-spline Path increases the minimum distance to the nearest obstacle by 0.78 compared to the Original Path and by 0.77 compared to the B-spline Path. Additionally, the average distance to the nearest obstacle for the Improved B-spline Path increases by 17.40% compared to the Original Path and by 14.93% compared to the B-Spline Path. Furthermore, the average curvature of the Improved B-spline Path decreases by 53.86% compared to the Original Path and by 28.54% compared to the B-spline Path. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Comparison to A* (100\%)**} \\ \cline{2-4}  & **Searched-Nodes (\%)** & **Length (\%)** & **Time (\%)** \\ \hline SSA-A* & 4.39 & 104.83 & 6.31 \\ Dijkstra & 320.17 & 100.00 & 94.08 \\ A*-JPS & 0.70 & 130.75 & 325.38 \\ ACO & 12.04 & 132.65 & 210.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of path planning algorithms in benchmark tests. Figure 12: Results of benchmark tests of five path planning algorithms on a 256 \(\times\) 256 map. (**a**) is the SSA-A*; (**b**) is the Classical A* Algorithm; (**c**) is Dijkstra’s Algorithm; (**d**) is the JPS-A* Algorithm; (**e**) is ACO. ## 5 Conclusions

To address the limitations of the traditional A* algorithm, such as low performance and unsmooth paths in path planning, this paper proposes a scheme that improves the A* algorithm by integrating the SSA for path planning, followed by path smoothing using the improved B-spline interpolation method. Through comparative experiments in various scenarios, including high-density clustered environments and low-density random environments, and multiple grid map scales (ranging from 20 \(\times\) 20 to 50 \(\times\) 50), the optimized algorithm demonstrates significant advantages in key indicators such as search efficiency, smoothness, and safety, offering a more efficient and reliable path planning approach for practical application scenarios. However, the SSA-A* still has certain limitations. While the SSA greatly enhances the optimization of the heuristic function, its performance can vary based on the complexity and specific characteristics of the environment. In environments with dynamic obstacles that change frequently, the algorithm may require more frequent re-optimization to adjust to new conditions. Additionally, the current implementation assumes a static environment, and extending it to handle real-time dynamic obstacle environments or 3D path planning requires further adjustments. Future research could focus on addressing these limitations. One potential direction is the development of adaptive mechanisms that dynamically adjust the SSA parameters based on real-time environmental changes, thereby improving the algorithm's robustness in dynamic and real-time scenarios. Another approach could involve exploring hybrid algorithms that integrate the SSA-A* with other advanced techniques, such as reinforce

Figure 13: Results of benchmark tests of five path smoothing algorithms on a 256 \(\times\) 256 map. (**a**) represents the original path, (**d**) is a detailed view of a portion of (**a**); (**b**) represents the path smoothed by B-spline, (**e**) is a detailed view of a portion of (**b**); (**c**) represents the path smoothed by the improved B-spline, and (**f**) is a detailed view of a portion of (**c**). ment learning or machine learning models, to further enhance path planning performance. Additionally, expanding the algorithm's scalability to handle more complex 3D path planning scenarios could be another key area for future development.