# Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm
## 4 Conclusions  
To address the problem of slow path planning by traditional A* algorithm and Theta* algorithm in a large scene environment, a W-Theta* algorithm is proposed, and its generation of discrete path points is optimized for trajectory processing. The following conclusions can be drawn from two comparison experiments:Compared with the traditional A* algorithm and the improved A*-based Theta* algorithm, the W-Theta* algorithm enables fast path planning in various complex environments by introducing a dynamic weighting strategy. Especially, the advantage is more obvious in the environment with larger map size, which solves the problem of slow path planning by traditional A* algorithm and Theta* algorithm in large scene environment. It is experimentally concluded that the W-Theta* algorithm reduces the path planning time by 81.65% on average compared with the Theta* algorithm, and by 79.59% on average compared with the A* algorithm.
2. To reduce unnecessary computations, the W-Theta* algorithm improves the algorithm performance by adding an additional value to the heuristic function to ensure uniqueness in planning the path, which allows it to control the traversal of nodes when planning the path and reduces the memory consumption during computation. According to the experimental data, it can be seen that the W-Theta* algorithm consumes an average of 44.31% less computer memory resources during computation than the Theta* algorithm, and an average of 29.33% less than the A* algorithm.
3. This is because the smaller the turn angle of the path is, the better the smoothing process is. In this paper, by using Euclidean distance for distance calculation, the total turn angle of the paths planned by the W-Theta* algorithm is reduced by 87.06, 77.67, and 34.57% on average with respect to the A* algorithm, Dijkstra algorithm, and Theta* algorithm. The paths planned by the W-Theta* algorithm are smoothed using the differential flattening method to fit the path points, which connects the sparse path points into smooth curves or dense trajectory points, ensuring the final paths are continuously smooth.
4. The paths planned by the W-Theta* algorithm are slightly longer than those of the original Theta* algorithm, so further optimization of the planned path length of the W-Theta* algorithm can be done.  
Conceptualization, Z.S.; methodology, Y.H.; software, Y.H. and J.L.; validation, Y.H., J.L. and Y.Z.; formal analysis, Y.Z.; investigation, Z.S.; resources, J.L.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, Y.H.; visualization, J.L.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Project of China Disabled Persons' Federation--on assistive technology: 2022 CDPFAT-01.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://movingai.com/](https://movingai.com/)]([https://movingai.com/](https://movingai.com/)).  
The authors declare no conflict of interest.