2022Unpublished venueOpen access

Improved A* Algorithm Based Path Planning for Mobile Robots

Shenghao Tong, Liuyang Guo, Boda Zhao, Cai He

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Abstract

An improved obstacle expansion algorithm is proposed to address the problem of too many traversing nodes and the lack of safety and smoothness of the solved paths in the traditional algorithm path planning process. A new map is generated by expanding the obstacles in the initial map, and the path planning is carried out in the new map to reduce the number of traversing nodes and improve the solution speed; the solution path is placed in the initial map to eliminate the redundant nodes; and the smoothness is optimized by the b-sample function. Simulation results show that the method is able to generate a safe and smooth planning path compared with traditional algorithms under the same environment.

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What this paper is about

An improved obstacle expansion algorithm is proposed to address the problem of too many traversing nodes and the lack of safety and smoothness of the solved paths in the traditional algorithm path planning process. A new map is generated by expanding the obstacles in the initial map, and the path planning is carried out in the new map to reduce the number of traversing nodes and improve the solution speed; the solution path is placed in the initial map to eliminate the redundant nodes; and the smoothness is optimized by the b-sample function. Simulation results show that the method is able to generate a safe and smooth planning path compared with traditional algorithms under the same environment.

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Available abstract

An improved obstacle expansion algorithm is proposed to address the problem of too many traversing nodes and the lack of safety and smoothness of the solved paths in the traditional algorithm path planning process. A new map is generated by expanding the obstacles in the initial map, and the path planning is carried out in the new map to reduce the number of traversing nodes and improve the solution speed; the solution path is placed in the initial map to eliminate the redundant nodes; and the smoothness is optimized by the b-sample function. Simulation results show that the method is able to generate a safe and smooth planning path compared with traditional algorithms under the same environment.

Key concepts: Traverse, Motion planning, Smoothness, Path (computing), Obstacle, Computer science, Algorithm, Any-angle path planning

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