基于改进A^(*)算法的路径规划方法研究

Research on path planning method based on enhanced A^(*)algorithm

针对A^(*)算法在路径搜索过程中,存在产生过多危险和复杂路径、陷入局部最优解等问题,提出一种A^(*)算法的改进方案。首先,通过引入评价函数的特殊动态权重动态调整算法搜索的精度和广度,提升算法效率。其次,在A^(*)算法子节点选择过程中加入规则判断,解决路线与障碍物顶点接触问题,避免高危险路径产生。再次,对A^(*)算法生成的路径进行平滑度优化,消除多余转角并使运动对象与障碍物保持一定安全距离,提升最终路径的平滑度和安全性。实验结果表明:对于不同复杂程度的障碍物环境,改进后的A^(*)算法都以更高的效率、更平滑和更安全的搜索方式找到路径,且大幅降低算法所占用数据存储空间。所提出的改进方案由于其出色的性能以及对于运动对象的安全性考量,有望在实际应用场景中取得良好的工程价值。

In addressing issues such as the generation of excessive dangerous and complex paths,as well as the potential entrapment in local optima during the path search process of the A^(*)algorithm,this paper proposes an enhanced method for A^(*).Firstly,the algorithm’s efficiency is improved by dynamically adjusting the precision and breadth of the algorithmic search through the introduction of a special dynamic weight adjustment algorithm for the evaluation function.Secondly,rules are incorporated into the A^(*)algorithm’s sub-node selection process to address issues related to the contact between the route and obstacle vertices,thereby preventing the generation of high-risk paths.Thirdly,a smoothness optimization is applied to the paths generated by the A^(*)algorithm,eliminating unnecessary turns and maintaining a safe distance between the moving object and obstacles,thereby enhancing the smoothness and safety of the final path.Experimental results demonstrate that the improved A^(*)algorithm exhibits higher efficiency,smoother and safer pathfinding in environments with varying degrees of obstacle complexity.Furthermore,the proposed improvements significantly reduce the data storage space occupied by the algorithm.The outstanding performance and safety considerations for the moving object make the proposed enhancements promising for achieving valuable engineering applications and garnering positive feedback from algorithm users in real-world scenarios.