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Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.
Li et al. (Wed,) studied this question.