# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 6 Conclusions  
This study proposes an AGV PP method that combines the APF algorithm with the ACA, making four improvements and innovations on the basis of the traditional ACA. In the algorithm optimization process of this study, an attraction function is added to the initial HF, which makes the target node have a stronger guiding effect on the ants performing the path-finding task, significantly improving the efficiency of the algorithm's convergence and accelerating the solution process. Considering the dynamic characteristics of the algorithm's iterative process, a method of flexibly adjusting the pheromone evaporation factor based on the number of iterations is adopted. This dynamic adjustment mechanism effectively expands the search range and greatly enhances the algorithm's ability to screen high-quality solutions in the entire space, comprehensively improving the algorithm's performance in solving complex problems. In the pheromone update process, a reward and punishment mechanism is added, and multiple parameters are used to dynamically adjust the pheromone increment, strengthening the guiding role of superior ants and weakening the misleading effect of inferior ants, thus avoiding getting trapped in local optima as much as possible. Finally, this paper uses the pruning method to further optimize the path, reducing unnecessary turning points, increasing the smoothness of the path, reducing unnecessary turns, and significantly reducing the overall length of the path.  
Through experiments on the improved ACA in grid environments of different complexities and comparisons with the traditional ACA in multiple aspects, the efficiency and stability of the algorithm proposed in this paper were verified. Subsequent localized path-planning studies will be conducted in conjunction with unknown environments and dynamic obstacles.  
Conceptualization, [PERSON] ([PERSON]) and Y.L. ([PERSON]); methodology, [PERSON] ([PERSON]); software, Y.L. ([PERSON]); validation, Y.L. ([PERSON]); formal analysis, Y.L. ([PERSON]); investigation, Y.L. ([PERSON]); resources, [PERSON] ([PERSON]); data curation, Y.L. ([PERSON]); writing  
Figure 11: Results of pruning in a \(30\times 30\) grid environment.  
original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]) After the joint review and confirmation of all the creators, the final version of the research results was unanimously recognized by the signed scholars and meets the requirements of academic integrity standards. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 72071130.  
Not applicable.  
All data are included in the article. Please contact the corresponding author if you need relevant data files.  
Thank you to the corresponding author [PERSON] for providing guidance and suggestions on the revision of the article.  
The authors declare no conflicts of interest.