In time-critical disaster scenarios, heterogeneous robotic teams combining unmanned aerial vehicles (UAVs) and unmanned ground vehicles(UGVs) have the potential to enhance situational awareness and improve navigation capabilities in complex indoor environments. This research presents a hierarchical mission planning approach designed for multi-agent search-and-rescue systems (SaRs), explicitly leveraging the distinct sensory and physical capabilities of UAVs and UGVs to maximise victim detection, area coverage, and energy efficiency. At the local control level, each SaR agent utilizes a heuristic controller based on fuzzy logic control (FLC) and the A* pathfinding algorithm, enabling efficient determination of individual targets and paths. At a higher level, a centralised supervisory controller employing model predictive control (MPC) coordinates global mission activities, intervening specifically when an agent’s capabilities become suboptimal within certain regions of the environment. Under randomised indoor SaR simulations featuring varied victim distributions, obstacle placements, and environmental conditions—including terrain complexity and visibility, the proposed cooperative approach consistently outperformed baseline methods. Although an exploration-focused heuristic approach (ACS) achieved slightly higher area coverage, the cooperative framework provided comparable coverage while achieving significantly higher victim detection performance, superior consistency, and better overall efficiency. Specialised scenario evaluations further illustrated that the cooperative approach can allocate and execute SaR-related search tasks more effectively, achieving superior operational performance compared to purely local-control-based (selfish) methods. These findings demonstrate that the hierarchical integration of heuristic local control and centralised MPC-based supervision, combined with the complementary capabilities of heterogeneous UAV-UGV teams, results in
robust, scalable, and efficient mission planning solutions suitable for complex, uncertain indoor SaR scenarios.