基于变步长A*与车身稳态转向模型的UGV路径规划

针对A*算法寻路时间长、生成的路径存在冗余折点的问题,本文提出一种基于车身稳态转向模型的变步长A*算法,首先通过设置子目标点的方式调节A*算法的搜索步长,减少寻路时间;其次在全局路径的折点处根据车身转向运动学约束进行局部重规划,从而得到一条易于跟踪的平滑路径;此外考虑到UGV (Unmanned Ground Vehicle,无人地面车辆)的实际宽度,改进后的算法还引入了障碍物延伸策略,使规划出的路径满足实际工程应用;最后通过仿真实验验证了本文改进算法的有效性,并与3种寻路算法进行对比,结果表明,本文改进的算法寻路时间更短、生成的路径更平滑,且与障碍物之间保持了安全距离.

UGV Path Planning Based on A* Algorithm of Variable Step Sizes and Steady-State Steering Model of Vehicles

In the A* algorithm, path finding is slow and the generated path has redundant turning points. For these reasons, an A* algorithm with variable step sizes based on the steady-state steering model of vehicles is proposed. Firstly, the search step size of the A* algorithm is adjusted by setting sub-targets to reduce path finding time. Secondly, local replanning is performed according to the kinematic constraints on vehicle steering at the turning points of the global path. Thus, a smooth path of easy tracking is obtained. In addition, considering the actual width of an Unmanned Ground Vehicle (UGV), the improved algorithm also introduces an obstacle extension strategy to make the planned path meet the actual engineering application. Finally, the proposed algorithm is proved effective. A comparison between this algorithm and three path finding algorithms shows that the improved algorithm has obvious advantages over the other three algorithms, including shorter path finding time, smoother paths, and safe distance from obstacles being maintained.