基于改进A^(*)算法的AGV路径规划研究

Research on AGV path planning based on improved A^(*)algorithm

针对传统A^(*)算法在AGV路径规划中存在遍历节点数和转弯次数较多问题,提出一种基于启发函数改进A^(*)算法。该算法采用加权曼哈顿距离作为启发函数,使得距离估计成本更接近最短距离,以减少算法遍历节点数;另外,在算法启发函数中引入转弯修正代价参数,从而减少路径转弯次数。MATLAB软件仿真实验结果表明,较传统A^(*)算法,基于启发函数改进A^(*)算法在AGV路径规划中能有效减少遍历节点数和路径转弯次数,提高AGV路径规划中路径搜索效率和路径平滑性。

In view of the problem that traditional A^(*) algorithm has many traversal nodes and turns in AGV path planning,an improved method of heuristic function in A^(*)algorithm was proposed.The algorithm uses the weighted Manhattan distance as the heuristic function,which makes the distance estimation cost closer to the minimum distance and reduces the number of nodes traversed by the algorithm;in addition,turning correction cost parameter was introduced into the algorithm heuristic function to reduce the number of path turns.MATLAB software simulation results show that,compared with the traditional A^(*)algorithm,the improved A^(*)algorithm based on heuristic function can effectively reduce the number of traversal nodes and the number of path turns in AGV path planning,thus improve the path search efficiency and path smoothness in AGV path planning.