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. 2023 Apr 27;8(2):182.
doi: 10.3390/biomimetics8020182.

Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm

Lisang Liu  1   2 , Jingrun Liang  1   2 , Kaiqi Guo  1   2 , Chengyang Ke  1   2 , Dongwei He  1   2 , Jian Chen  1   2
Affiliations

Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm

Lisang Liu et al. Biomimetics (Basel). .

Abstract

Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strategies. Firstly, Cauchy reverse learning was used to initialize the sparrow population to avoid a premature convergence of the algorithm. Secondly, the sine-cosine algorithm was used to update the producers' position of the sparrow population and balance the global search and local exploration capabilities of the algorithm. Then, a Lévy flight strategy was used to update the scroungers' position to avoid the algorithm falling into the local optimum. Finally, the improved SSA and dynamic window approach (DWA) were combined to enhance the local obstacle avoidance ability of the algorithm. The proposed novel algorithm is named ISSA-DWA. Compared with the traditional SSA, the path length, path turning times and execution time planned by the ISSA-DWA are reduced by 13.42%, 63.02% and 51.35%, respectively, and the path smoothness is improved by 62.29%. The experimental results show that the ISSA-DWA proposed in this paper can not only solve the shortcomings of the SSA but can also plan a highly smooth path safely and efficiently in the complex dynamic obstacle environment.

Keywords: Cauchy reverse learning; Lévy flight strategy; dynamic window approach; path planning; sine–cosine algorithm; sparrow search algorithm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the standard SSA.
Figure 2
Figure 2
Iterative process of the ILPS strategy.
Figure 3
Figure 3
Iterative process of the INSS strategy.
Figure 4
Figure 4
Flow chart of the ISSA algorithm.
Figure 5
Figure 5
Three environmental models for path planning of the mobile robot: (a) environment model 1 (ENV. 1); (b) environment model 2 (ENV. 2); (c) environment model 3 (ENV. 3). The green dot represents the start point and the red dot represents the end point.
Figure 6
Figure 6
Path planned by the algorithms (ACO, MRFO, WOA, SSA, ISSA and ISSA-DWA) in environment model 1: (a) path planned by ACO; (b) path planned by MRFO; (c) path planned by WOA; (d) path planned by SSA; (e) path planned by ISSA; (f) path planned by ISSA-DWA. The green dot represents the start point and the red dot represents the end point.
Figure 7
Figure 7
Convergence curve of the algorithms (ACO, MRFO, WOA, SSA, ISSA) in environment model 1.
Figure 8
Figure 8
Path planned by the algorithms (ACO, MRFO, WOA, SSA, ISSA and ISSA-DWA) in environment model 2: (a) path planned by ACO; (b) path planned by MRFO; (c) path planned by WOA; (d) path planned by SSA; (e) path planned by ISSA; (f) path planned by ISSA-DWA. The green dot represents the start point and the red dot represents the end point.
Figure 9
Figure 9
Convergence curve of the algorithms (ACO, MRFO, WOA, SSA, ISSA) in environment model 2.
Figure 10
Figure 10
Path planned by the algorithms (ACO, MRFO, WOA, SSA, ISSA and ISSA-DWA) in environment 3: (a) path planned by ACO; (b) path planned by MRFO; (c) path planned by WOA; (d) path planned by SSA; (e) path planned by ISSA; (f) path planned by ISSA-DWA. The green dot represents the start point and the red dot represents the end point.
Figure 11
Figure 11
Convergence curve of the algorithms (ACO, MRFO, WOA, SSA, ISSA) in environment model 3.
Figure 12
Figure 12
Local dynamic obstacle avoidance effect of ISSA-DWA in environment model 1: (a) the initial state; (b) the avoiding state; (c) the successful state.
Figure 13
Figure 13
Local dynamic obstacle avoidance effect of ISSA-DWA in environment model 2: (a) the initial state; (b) the avoiding state; (c) the successful state.
Figure 14
Figure 14
Local dynamic obstacle avoidance effect of ISSA-DWA in environment model 3: (a) the initial state; (b) the avoiding state; (c) the successful state.
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References

    1. Sánchez-Ibáñez J.R., Pérez-del-Pulgar C.J., García-Cerezo A. Path Planning for Autonomous Mobile Robots: A Review. Sensors. 2021;21:7898. doi: 10.3390/s21237898. - DOI - PMC - PubMed
    1. Zhu D.-D., Sun J.-Q. A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute. IEEE Access. 2021;9:19761–19775. doi: 10.1109/ACCESS.2021.3053169. - DOI
    1. Zhang Z., Wu J., Dai J., He C. Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment. Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng. 2021;236:72–81. doi: 10.1177/09544100211007381. - DOI
    1. Wang Z., Sun H., Cai P., Lan X., Wu D. Multi-point traversal path planning of manipulator based on improved RRT algorithm; Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence; Shanghai, China. 20–22 September 2019.
    1. Cao K., Cheng Q., Gao S., Chen Y., Chen C. Improved PRM for Path Planning in Narrow Passages; Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA); Tianjin, China. 4–7 August 2019.