# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
## 4 Results of the Experiment and Discussion
### Robot Path Planning Based on TSDBO Algorithm
#### 4.5.3 Two-Dimensional Map Model of 10 \(\times\) 10  
There are 20 iterations and a population size of 100 in the 10 \(\times\) 10 map. DBO, GODBO, and MSADBO are selected as competitors. The optimal path for each algorithm in a 10 \(\times\) 10 map is shown in Figure 10. To eliminate chance, each algorithm is run 10 times, and the average path, shortest path, longest path, and average time of each algorithm are recorded. From Figure 10, we can see that TSDBO has the shortest path, while DBO is clearly in the local optimum. As illustrated in Table 4, in terms of shortest path, average path, and longest path, TSDBO performs better than any other method. Although the TSDBO algorithm is a little longer than the DBO algorithm in terms of average time, these times are negligible considering the improvement of the optimization strategy. And the shortest path of TSDBO is 5.5% better than the shortest path  
Figure 8: Processed raster map.  
Figure 9: Robot movement direction.  
t-distribution perturbation, which improves the search efficiency of the algorithm in the later stage. The three improvement strategies enhance the performance of the algorithm from the dimensions of population diversity, global exploration, and local exploitation, respectively. The algorithm's total speed is increased by the focused enhancements at each stage, which allow it to easily identify the global optimal region and conduct effective searches within it.  
In order to demonstrate the effectiveness of the strategy's introduction, we evaluate TSDBO against a number of rivals in 12 benchmark functions. According to the experimental findings, TSDBO performs better than the original DBO and other rival algorithms. Additionally, TSDBO outperforms the competing algorithms in robot path planning, reducing the shortest path length by 5.5-7.2% on 10 \(\times\) 10 2D maps and 11.9-14.6% on 20 \(\times\) 20 2D maps. These results demonstrate the effectiveness and dependability of TSDBO in resolving practical issues in addition to confirming the strategy's introduction.  
Although TSDBO demonstrates excellent performance, the added optimization strategies increase processing time compared to the basic algorithm. However, in a complex 20 \(\times\) 20 path planning environment, TSDBO reduces computation time and improves stability. The Dung Beetle Optimization Algorithm proposed in this study achieves better results in 2D path planning, but still suffers from the problem of locally optimal solutions. Future work will focus on the following directions: first, to address the local optimization problem of the algorithm, the global search capability of the algorithm can be further improved by adopting a more advanced adaptive mechanism. Second, considering that the current experiments are limited to static obstacle environments, the algorithm can be extended to dynamic obstacle environments in the future and combined with a real-time update mechanism to improve the dynamic adaptability of path planning. In addition, applying the improved algorithm to more complex 3D path planning problems and further verifying its performance in complex scenarios are important research directions in the future.  
Conceptualization and methodology, K.L. and Y.D.; data curation, K.L.; writing--original draft preparation, K.L. and Y.D.; writing--review and editing, K.L., Y.D. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Innovation Fund for Higher Educational Institutions in Gansu Province (2022B-107,2019A-056) of Yongqiang Dai, the Youth Tutor Fund of Gansu Agricultural University (GAU-QDFC-2019-02) of Yongqiang Dai, and the Natural Science Foundation of Gansu Province (20 JR10 RA510,1506 RJZA007) of Yongqiang Dai.  
Not applicable.  
Research data are available upon reasonable request by contacting the corresponding author. Data usage must comply with our ethics approval.  
The authors thank the editor and reviewers for their valuable suggestions.  
The authors declare no conflicts of interest.