对AGV路径规划A星算法的改进与验证

为了减少AGV（Automate Guide Vehicle，自动导引车）的运输路径长度和转折次数，提出了改进的A星算法，采用几何方法对传统A星算法规划出的路径进行进一步优化。首先遍历路径上的所有节点，剔除路径中冗余节点和不必要拐点，获取仅包含起点、必要拐点、终点的路径。最后计算AGV在拐点处的旋转角度及旋转方向，使AGV在拐点处能够调整自身姿态。并分别对传统A星算法、蚁群算法和改进A星算法进行了对比实验。实验结果表明该方法不仅保留了A星算法运算速度快的优点，还能够有效地规划出距离短且平滑的路径。提高了AGV的运行效率，降低了AGV的耗能。

Improvement and verification of A-star algorithm for AGV path planning

To reduce the length of the path and the number of turns of the Automate Guide Vehicle（AGV）, an improved A-star algorithm is proposed. The path planned by the traditional A-star algorithm is further optimized by geometric methods. Firstly, based on the initial path planned by A-star algorithm, traversing all the nodes on the initial path, removing redundant inflection points and redundant nodes and obtainning the path that contains the starting node, the end node, and the key inflection points. Then the rotation direction and rotation angle of AGV at the inflection point are calculated to adjust the posture of the AGV. Comparative experiments on traditional A-star algorithm, ant colony algorithm and improved A-star algorithm are carried out respectively. And the experimental results demonstrate that the method not only has a fast calculation speed but also can provide a short and smooth panth.