Emergency response missions employing heterogeneous multi-UAVs are challenged by both the tight coupling between task assignment and motion control and the inherent difficulty in obtaining full probability distributions of stochastic disturbances. In practice, only partial moment information—typically the mean, covariance, and support set—can be estimated. To address this, the paper proposes a distributionally robust integrated “decision–control” framework. A bi-level optimization model is established: the upper level minimizes the maximum mission completion time across all platforms, while the lower level solves minimum-time optimal control problems under kinematic constraints, with the two levels coupled through task execution times. Given only the mean, covariance, and support set of disturbances, an ambiguity set is constructed, and by leveraging duality theory and semidefinite programming, the distributionally robust chance constraints on site reachability are equivalently transformed into deterministic safety margins. A two-stage trajectory planning method is further designed to decouple accumulated time estimation from robust constraint enforcement, ensuring computational tractability. Simulation results across multiple disturbance configurations show that, whereas deterministic planning yields an overall mission success rate of only about 0.7%, the proposed framework consistently achieves success rates above 99.9% and effectively balances workload among multiple UAVs. These results validate the practical benefit of the framework in providing reliable emergency response plans under limited distributional information.

This paper presents a method for determining weight coefficients in multi-objective UAV task assignment under a competitive data-collection setting, where two UAV companies independently visit each other’s sensor nodes. In this setting, each company expresses operational intent through weights for data value, distance utility, and flight safety. Our own weights are prescribed by higher-level planning, whereas the competitor’s latent weights must be estimated from observed visit assignments. To estimate these weights, we apply Approximate Bayesian-Inspired Inference, which also provides a measure of the associated inference uncertainty. Subsequently, a purpose-built fuzzy rule base translates the inferred weights into response weights, while the fuzziness of this mapping is quantified as a second source of uncertainty. From these two uncertainty measures, an adaptive coefficient is derived that blends the planning and response weights into the final task assignment. Under controlled synthetic settings, numerical experiments demonstrate that the inference procedure outperforms direct loss-minimization baselines in both accuracy and speed, and that the adaptive fusion effectively balances planning adherence with responsiveness to competitor behavior.