<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...

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Online veröffentlicht: 21. Juli 2024

Seitenbereich: 67 - 81

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

Schlüsselwörter
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.

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Online veröffentlicht: 21. Juli 2024

Seitenbereich: 67 - 81

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

SchlüsselwörterMobile 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.

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