… this thesis reconceptualizes how robots priorize critical tasks when resources become constrained. The approach classifies robot acons according to their objective’s importance and implements multiple cost modes to handle varying environmental conditions. The second contribution is the development of a single-robot framework based on Monte-Carlo Tree-Search that demonstrates increased objective achievement in normal environments while guaranteeing critical objective execution during exceptional conditions. The third contribution is extending this solution to multi-robot systems through an approach that includes robot partitioning strategies and a robust synchronization process for online replanning, enabling robots to adapt to changing conditions in real-me. This multi-robot implementation called RESCUE tackles the additional challenge of preventing objective duplication across robots while maintaining system flexibility. This approach is also generalized to multiple levels of criticality. Finally, the contributions are evaluated through simulation by comparing them to existing Monte-Carlo Tree-Search solutions. The experimental results validate that the framework successfully balances competing priories: maximizing objective completion during normal operation while ensuring critical task execution during environmental challenges. These contributions advance the field of adaptive planning for uncertain robotic environments with objective criticality by providing a more robust and resilient approach to resource allocation in the face of unpredictable conditions.