如今，大规模自然灾害已成为重要的世界性的社会和政治问题。灾害发生后，许多救援任务，如从废墟中救出幸存者，扑灭建筑物火灾等，需要救援人员到指定地点去完成。然而，由于灾后环境的未知性和复杂性，救援人员很难快速找到环境中救援任务地点（目标点）。在这种情况下，搜索人员（移动对象）通过在灾后环境中搜索并组建Ad-hoc网络为救援人员收集与传递救援任务信息。然而，在现有的大部分移动对象搜索与Ad-hoc网络组建方面的研究并没有考虑到环境未知、复杂的特点，无法满足移动对象搜索与Ad-hoc网络组建的需求。为此，本项目拟开展对未知、复杂环境中移动对象搜索、Ad-hoc网络组建与调整方法的研究，基于agent的移动对象和栅格化的环境建模，提出相关方法来使移动对象实现在未知、复杂环境中搜索、Ad-hoc网络组建和调整。 同时，我们还将通过专业的城市灾难仿真平台RCRSS和真实测试来验证、改进我们所提出的方法。

Nowadays, large-scale disasters throughout the world have become important social and political concerns. After disasters happened, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, etc. need first responders to complete on site. However, due to the unknown and complexity of disaster environments, it is hard for the first responders to find stationary tasks. In such circumstances, search personnel (i.e., mobile objects) are usually search and establish ad hoc network in disaster environments so as to collect and transmit information of stationary tasks to first responders. However, most of current moving agent-based approaches for searching and ad-hoc establishment and adjustment do not consider the unknown and complexity of disaster environments, so they cannot satisfy the requirements of mobile objects working in unknown and complexity environments. To this end, this project will research on searching, ad-hoc network establishment and adjustment of mobile objects in unknown and complexity environments. Based on the concept of agents to describe the mobile objects, we will propose corresponding approaches to guide mobile objects to efficiently search and establish ad-hoc network in unknown and complexity environments. At the same time, the proposed approaches will be evaluated on the simulation platform of RoboCup Rescue Simulation System and real environments.