# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 5 Conclusions  
Because the classical A* algorithm has some limitations in the field of path planning, especially in dealing with the problems of obstacle corner collision, unsmooth path, and sharp turning point when the diagonal moves, it is difficult to adapt to the motion characteristics of the actual mobile robot. In the specific application scenario of underground  
Figure 16: Static obstacle avoidance experiment on the Ankai map.  
Figure 17: Dynamic obstacle avoidance experiment on the Ankai map.  
roadway planning, the path planned by the algorithm is often close to the edge of the roadway, which increases the potential collision risk.  
Therefore, in view of the above problems, this paper proposes an optimized adaptive A* algorithm. Firstly, the algorithm defines the concept of the intersection point. By checking the state of two adjacent grids on the other side of the intersection point, the collision problem when the diagonal line moves is effectively avoided. Secondly, the factor of roadway width is integrated into the heuristic function, which makes the path planning algorithm more inclined to the middle area of the roadway when selecting the path, thus reducing the collision risk. At the same time, the adaptive B-spline curve is used to dynamically adjust the order and smoothing factor according to the complexity of the global path, and the path is smoothed.  
Then, the improved A* algorithm is combined with the improved DWA algorithm to achieve dynamic obstacle avoidance. Finally, the experiment proves that the path planned by the optimized A* algorithm is not only closer to the center of the roadway, but also the path is smooth, and the number of inflection points is greatly reduced. Compared with the original algorithm, the optimization algorithm can avoid collisions with the obstacle corner when the diagonal moves, which is more in line with the actual motion requirements of the mobile robot. At the same time, the fused path planning algorithm can avoid the new static obstacles and dynamic obstacles, and meet the requirements of dynamic obstacle avoidance. In addition, we conducted supplementary experiments on the new Ankai underground roadway map to further verify the effectiveness and applicability of the proposed algorithm in complex underground roadway environments.  
[PERSON] and [PERSON] jointly contributed to the conceptualization of the research. [PERSON] and [PERSON] co-developed the methodology, with [PERSON] specifically responsible for software implementation. The validation and analytical processes were collaboratively performed by [PERSON] and [PERSON] and [PERSON] conducted the data curation and management. The original manuscript draft was prepared by [PERSON], while [PERSON] and [PERSON] jointly handled the review and editing processes. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number No. 2023 YFC2907405.  
Not applicable.  
The data presented in this study are available in the article.  
It is particularly noteworthy that [PERSON] provided critical academic guidance throughout key stages of this study, including experimental design, manuscript development, and journal submission processes. Her professional insights significantly contributed to the successful execution and completion of this research project.  
Authors [PERSON] and [PERSON] were employed by the company Hunan Chuangyuan High-Tech Machinery Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.