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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。
After the earthquake, the city’s road conditions are unknown and complicated. Therefore, how to quickly find the shortest path to save more casualties after the earthquake in the robot rescue has become a hot issue. An ant colony optimization algorithm based on dynamic path planning is proposed to find the shortest path in the dynamically changing post-earthquake rescue environment to reduce the rescue time. It uses the global information of the original urban traffic map to establish the target attracting function, guides the ants’ route searching under the complex dynamic environment, increases the probability of selecting neighbors that are closer to the target point, and decreases the probability of ants choosing the non-shortest path. By comparing with the MMAS algorithm, it is verified that the proposed algorithm can converge to the shortest path faster and has better dynamic performance.