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Abstract

To address the global parking problem in complex environments, a fusion algorithm is proposed. The Rapidly-exploring Random Tree (RRT) algorithm is utilized to guide the Dynamic Window Approach (DWA) in planning the global path. The Reeds-Shepp (RS) curve is introduced to calculate intermediate points, thus obtaining a complete parking path. First, considering the limitations of the traditional RRT algorithm, such as low search efficiency, circuitous paths, and redundant nodes, methods including biased sampling, adaptive step-size, and node re-connection are introduced, which improve the path-planning efficiency. Second, the DWA algorithm is optimized by introducing a dynamic target-distance weighted function, enhancing the efficiency of the paths planned by DWA. Additionally, the Artificial Potential Field (APF) method is employed to optimize the guiding points that are too close to obstacles. Finally, intermediate parking points are calculated according to the actual parking-space conditions, and the parking process is completed using the Reeds-Shepp curve. The path is simulated using Simulink and Carsim, and the simulation results indicate that this algorithm can efficiently plan collision-free paths and performs well in parking environments with complex obstacles.

Keywords

Fusion algorithm RRT DWA artificial potential field path planning automatic parking

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Fusion of improved RRT and dynamic window algorithms for automatic parking path planning

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