  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.
