Path planning is widely used in today's life, with the A* algorithm being one of the principal methods. As applications become more complex, improving the efficiency of robot path planning has become a crucial challenge. This article mainly introduces the use of the A* algorithm in path planning. The article first introduces the basic logic and expression formula of the A* algorithm. Secondly, the article introduces the application of the A* algorithm in the robot module of MATLAB, demonstrating the route exploration of robots under the A* algorithm. Finally, the article provides a detailed introduction to the cutting-edge research achievements of the A* algorithm, mainly focusing on how to improve the algorithm, shortens time, and enhance efficiency. The article introduces four different methods: Slope comparison method; Reduce node method; Bidirectional search method; the jump point search method. Slope comparison method identifies obstacles on a point-to-point straight line, take the shortest path to reach the obstacle, and then re plan in front of the obstacle to repeatedly reach the endpoint. Reduce node method improves efficiency by decreasing the number of nodes while maintaining straight line segments in the path. Bidirectional search method uses a dual search approach of endpoint and starting point to find duplicate routes to improve efficiency. The jump point search method improves efficiency by identifying and omitting irrelevant nodes along the path based on symmetry in the grid-based maps.
Jingbo Ni (Tue,) studied this question.
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