Urban search and rescue (USaR) missions operate in volatile environments where time, information, and energy are scarce. This thesis presents a hierarchical decision-making and coordination architecture that elevates heterogeneous robotic autonomy from local actions to mission-level reasoning. Robots are modeled by capabilities of action and perception rather than fixed roles, allowing robot-specific suitability to be evaluated for any tasks that evolve during operation considering mission metrics. Sequential mission planning is formulated as a nonlinear model predictive control (MPC) problem with safe-return constraints over a finite horizon. Task suitability is first estimated by a fuzzy inference system that fuses robot capabilities with task-specific priorities. A multi-criteria decision-making (MCDM) framework assigns mission utilities to candidate actions by weighing urgency, information gain, and inter-task preferences, providing flexibility when task definitions are ambiguous and priorities shift.

The decision process is implemented in a two-tier hierarchial control scheme. Locally, each robot solves a tractable linearization of the MPC by filtering independent actions using utilities and observation overlaps, followed by a mixed-integer linear programming (MILP) scheduler subject to energy-aware safe-return constraints. Globally, a multi-robot extension reconciles local plans to produce a consistent allocation that balances distributed adaptability with centralized coordination.

The architecture is validated in realistic simulations with heterogeneous robots and evolving tasks. Relative to heuristic baselines, it accelerates victim identification and area mapping while achieving higher utility in most of 17 randomized single-robot scenarios. In multi-robot experiments, it allocates high-priority tasks to the most suitable platforms and completes missions faster than non-cooperative approaches, while using fewer computational resources than comparable optimization-based methods.