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. 2024 Jun 11;9(6):351.
doi: 10.3390/biomimetics9060351.

Application of Improved Sparrow Search Algorithm to Path Planning of Mobile Robots

Yong Xu  1 , Bicong Sang  1 , Yi Zhang  1
Affiliations

Application of Improved Sparrow Search Algorithm to Path Planning of Mobile Robots

Yong Xu et al. Biomimetics (Basel). .

Abstract

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning.

Keywords: Lévy flight strategy; adaptive T-distribution variation strategy; circle chaotic mapping; integration of northern goshawk exploration phase location strategy; path planning; sparrow search algorithm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Classification of path-planning algorithms.
Figure 2
Figure 2
Circle mapping.
Figure 3
Figure 3
Ten images of test functions.
Figure 4
Figure 4
Convergence curves of 10 benchmark functions.
Figure 5
Figure 5
Comparison of 10 test function boxplots.
Figure 6
Figure 6
Convergence curves of 10 test functions for three algorithms.
Figure 7
Figure 7
Comparison of 12 test function boxplots.
Figure 8
Figure 8
Ablation experiment results’ convergence curves.
Figure 8
Figure 8
Ablation experiment results’ convergence curves.
Figure 9
Figure 9
Ablation experiment results’ boxplots.
Figure 10
Figure 10
Map environment model.
Figure 11
Figure 11
Robot walking path diagram: (a) travel path; (b) correct path; (c) error Path; (d) correct path.
Figure 12
Figure 12
Global path-planning method based on ISSA.
Figure 13
Figure 13
Path planning of six algorithms in environment model 1.
Figure 14
Figure 14
Path planning of six algorithms in environment model 2.
Figure 15
Figure 15
Path planning of six algorithms in environment model 3.
Figure 16
Figure 16
Path planning of six algorithms in environment model 4.
Figure 17
Figure 17
Path planning of six algorithms in environment model 5.
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