<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.</p>
</abstract>

During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.

An Improved Hybrid Path Planning Algorithm in Indoor Environment

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"An Improved Hybrid Path Planning Algorithm in Indoor Environment"}

During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in...

Algorithm	Path length/m	Search time/s	Does the algorithm have the ability to handle dynamic obstacles
A-Star	45.36	6.72	No
IAPF	48.00	10.43	Yes
DWA	48.86	28.21	Yes
Hybrid algorithm	46.54	8.14	Yes

Algorithm	Path length/m	Time for path finding/s	Number of expansion nodes	Is there a turning point
A-Star	22.42	5.9	166	Yes
Improved A-Star	21.56	5.5	59	No

Path planning algorithm	Path length/m	Number of nodes passed through	Search time/s
A-Star Hybrid DWA	3.66	126	54.42
Hybrid algorithm in this paper	3.24	92	48.36

Experiment Name	Algorithm	Path length/m	Run time/s	Number of cycles
Path planning testing	APF	49.970710	6.186677	447
Path planning testing	IAPF	48.003037	5.430491	440

Complex obstacle testing	APF	∞	∞	∞
Complex obstacle testing	IAPF	51.519690	6.801836	451

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Pubblicato online: 21 lug 2024

Pagine: 67 - 81

DOI: https://doi.org/10.2478/ijanmc-2024-0017

Parole chiave
Mobile Robot, Path Planning Technology, A-Star Algorithm, Artificial Potential Field Algorithm, Autonomous Obstacle Avoidance

© 2024 Jiaxiang Fang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Figure 15.

Algorithm comparison in static environment

Comparison of the effects of A-Star algorithm improvement

Results of algorithm comparison in real environment

Comparison results of improved algorithm

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Pubblicato online: 21 lug 2024

Pagine: 67 - 81

DOI: https://doi.org/10.2478/ijanmc-2024-0017

Parole chiaveMobile Robot, Path Planning Technology, A-Star Algorithm, Artificial Potential Field Algorithm, Autonomous Obstacle Avoidance

© 2024 Jiaxiang Fang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

Figure 13.

Figure 14.

Figure 15.

Algorithm comparison in static environment

Comparison of the effects of A-Star algorithm improvement

Results of algorithm comparison in real environment

Comparison results of improved algorithm

Parole chiave
Mobile Robot, Path Planning Technology, A-Star Algorithm, Artificial Potential Field Algorithm, Autonomous Obstacle Avoidance