基于人群密度的最优路径规划算法研究

Research on optimal path planning algorithm based on crowd density

在众多路径规划算法中,A^*算法是一种典型的最短路径规划算法,但是该算法的应用环境较为局限。为此综合复杂环境中人群密度的因素,以时间最短为准则,将最短距离路径规划问题优化为最短时间路径规划。最优路径规划算法在A^*算法的基础上,引入不同人群密度环境下的行人速度模型,将A^*算法中基于距离的评估函数改变为基于时间的评估函数。实验通过标准网格地图对144种情况进行了模拟,结果表明,相较于传统A^*算法,最优路径规划算法优先选择从周围低人群密度区域绕行到达终点,规划路径在距离上可能更长,但是花费的时间更短。

Among many path planning algorithms, A^* algorithm is a typical shortest path planning algorithm, but the application environment of this algorithm is more limited. This paper synthesizes the factors of crowd density in complex environments, and optimizes the shortest distance path planning problem to the shortest time path planning with the shortest time as the criterion. Based on the A^* algorithm, the optimal path planning algorithm introduces the pedestrian velocity model in different crowd density environments, and changes the distance-based evaluation function in the A^* algorithm to a time-based evaluation function. The experiment simulates 144 cases through the standard grid map. The results show that compared with the traditional A^* algorithm, the optimal path planning algorithm preferentially chooses to detour from the surrounding low crowd density area to the end point. The planned path of the algorithm is at the distance. It may be longer, but it takes less time.