Mobile Robot Path Planning Considering Obstacle Gap Features
In order to fully harness obstacle information in path planning and improve the coordination between global and local path planning, a novel mobile robot path planning method is proposed. The novelty of the proposed path planning strategy lies in its integration of obstacle gap characteristics into...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5979 |
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| Summary: | In order to fully harness obstacle information in path planning and improve the coordination between global and local path planning, a novel mobile robot path planning method is proposed. The novelty of the proposed path planning strategy lies in its integration of obstacle gap characteristics into both global and local planning processes. Specifically, this method addresses the issues of low search efficiency, excessive redundant points, and poor path quality in the traditional A* algorithm for global path planning by extracting gap grids in the global grid map and incorporating their influence into the heuristic function, thereby guiding the search more effectively. The generated global path is further optimized at gap points to remove redundant nodes. For local path planning, which employs the Dynamic Window Approach (DWA) and often exhibits weak compatibility with global planning and a lack of smoothness through obstacle gaps, this method calculates feasible steering angles based on the distance between the robot and obstacles as well as gap attributes. Additionally, the geometric relationship between global and local paths is established using the Bernstein equation, generating segmented guidance control points for DWA. Simulation experiments demonstrate that the proposed algorithm significantly enhances path efficiency and obstacle avoidance capability in tight space environments, reducing path length by approximately 4.79% and motion time by approximately 15.22% compared to conventional algorithms. |
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| ISSN: | 2076-3417 |