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地震发生后城市的道路状况未知而且复杂多变,因此,在震后机器人救援中,如何快速地找到最短路径以拯救更多的伤员,成为研究的热点问题。提出一种目标吸引的动态路径规划蚁群算法,在动态变化的震后救援环境中找到最短路径,减少救援时间。利用原有城市交通地图的全局信息建立目标吸引函数,对蚂蚁在复杂动态环境下的路径搜索进行引导,提高其选择离目标点更近邻节点的概率,减小蚂蚁对非最短路径的选择概率。通过与MMAS算法进行仿真实验对比,验证了提出的算法可以更快地收敛到最短路径并具有较好的动态性能。“,”The road condition of city is unknown,complex and changing,so in post earthquake robot rescue,how to find the shortest path quickly to save more wounded persons becomes a hot issue.A target attraction based ant colony for dynamic path planning was proposed to find the shortest path in the post earthquake environment to reduce the rescue time.And the global information of city traffic map was adopted to establish target attraction function,which guided the ants for path searching in the complex dynamic environment,to improve the probability of selecting the closer path to the target point,and reduce the probability of selecting the non-shortest path.Comparing with MMAS algorithm through simulation,the proposed TAAC algorithm was verified to have a good dynamic performance and could converge to the shortest path quickly.