针对城市地震等大规模灾害发生后,灾难救援搜救范围大、现场情况多变、远距离通讯交流阻塞、施救人员危险性高等问题,提出了一种使用具有特定功能的多智能体开展救援任务,并利用分区的思想,将多智能体分配到城市各个区域,以扩大搜救范围,节省救援时间的方案.同时,针对传统K-means聚类方法的不足,提出了一种基于ISODATA(迭代自组织数据分析)算法的多智能体任务分配策略,以根据不同的城市环境进行自适应聚类划分.实验结果表明,该方法不仅比传统的K-means分区算法有更好的救援效果,而且大大促进了的整体救援效果.%After an earthquake, it will cause great damage to urban road traffic, housing construction and people's life safety.It is the most important thing for assigning the rescue team to arrive at the disaster scene as soon as possible.But in an actual large-scale earthquake, these are quite a few difficulties for search and rescue task.For example, complex situation,long-distance communication blocking and the high risk for human rescue.In order to solve these complex and difficult problems.The paper proposes that ad hoc agents carry out rescue task, and assign agents to every area of the city by using the idea of clustering.Meanwhile, due to the shortage of traditional clustering methods like K-means, the paper proposes an allocation strategy of multi-agent based on ISODATA Algorithm, firstly cluster different urban environment adaptively, then assign search and rescue team to the corresponding region, the search and rescue team will carry out rescue at last.The experiments demonstrate that this method not only has a better performance compared with K-means, but also has a better performance in the whole rescue.
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