In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects but overlook crucial elements like information gathering, task assignment, and planning. Furthermore, previous methods considering robot capabilities and victim requirements suffer from time complexity due to repetitive planning steps. To overcome these challenges, we introduce a comprehensive framework__the Multi-Stage Multi-Robot Task Assignment. This framework integrates scouting, task assignment, and path-planning stages, optimizing task allocation based on robot capabilities, victim requirements, and past robot performance. Our iterative approach ensures objective fulfillment within problem constraints. Evaluation across four maps, comparing with a state-of-the-art baseline, demonstrates our algorithm's superiority with a remarkable 97 percent performance increase. Our code is open-sourced to enable result replication.

翻译：在灾后场景中，高效的搜救行动需要机器人与人类之间的协同协作。现有规划方法虽聚焦于特定方面，却忽略了信息收集、任务分配与规划等关键要素。此外，以往考虑机器人能力与受困者需求的方法，因重复规划步骤而面临时间复杂度过高的问题。为克服这些挑战，我们提出一个综合框架——多阶段多机器人任务分配。该框架整合了侦察、任务分配与路径规划三个阶段，基于机器人能力、受困者需求及历史表现优化任务分配。我们采用的迭代方法确保在问题约束下实现目标达成。在四张地图上的评估显示，与现有最优基线相比，我们的算法性能提升高达97%。我们已开源代码以支持结果复现。