Risk-Aware Task Allocation and Scheduling Considering Platform States for Heterogeneous Unmanned Systems

High-level mission scheduling for heterogeneous UAV–UGV teams must account for travel efficiency, environmental exposure, platform condition, and task compatibility. We propose a global incremental scheduler that combines the normalized travel distance, platform-dependent heuristic risk, and battery-based state while enforcing compatibility and battery constraints. Across matched simulations, the method adapted assignments to risk and degraded platforms. Additional benchmarking found a quality–runtime trade-off: a genetic algorithm baseline achieved a lower mean small-case objective gap, whereas the proposed method ran much faster in the tested moderate cases. Risk parameter sensitivity and a synthetic extended-energy stress test characterized the limits of the observed adaptation. Exact-certified restrictive cases exposed greedy dead ends, and a separate bounded repair recovered most of them. A separate time-aware comparator reduced the makespan in many paired cases at an average increase in the original mission cost. The results characterize an offline, high-level heuristic rather than a globally optimal or physically validated deployment method.