Wind disturbances in the thin Martian are highly stochastic and spatially correlated, posing significant challenges to rotorcraft flight safety. Meanwhile, limited onboard computational resources make real-time safe trajectory planning difficult. This paper proposes a trajectory planning and analysis framework based on composite wind modeling and differential flatness theory. The proposed method decouples deterministic trajectory generation from stochastic robustness evaluation, enabling efficient trajectory planning in uncertain wind environments with planning-stage robustness evaluation. First, a composite wind model is constructed by combining multi-scale decomposition with Gaussian process regression, where a local kernel strategy is introduced to improve computational efficiency. Second, based on differential flatness theory and the MinCo method, a high-order polynomial trajectory is formulated. A multi-dimensional cost function is designed under wind field constraints to generate a nominal trajectory, incorporating trajectory smoothness, flight time, energy consumption, risk avoidance, and state feasibility. Finally, wind uncertainty is propagated via Monte Carlo sampling, and trajectory performance is quantitatively evaluated. Simulation results show the proposed method can generate safe, feasible, and risk-aware trajectories under the uncertain Martian wind conditions. Across all test scenarios, the mean terminal deviation, average deviation, and maximum deviation remain within 2.1%, 1.6%, and 3.6% of the trajectory length, respectively, indicating strong robustness in the evaluated scenarios.

Rotor dynamic inflow modeling is essential for rotorcraft flight dynamics analysis and control, particularly under the rarefied Martian atmospheric conditions. This study develops a reduced-order state-space dynamic inflow model for Mars coaxial rotors based on the Pitt–Peters formulation, incorporating aerodynamic coupling between the upper and lower rotors. High-fidelity unsteady aerodynamic responses are obtained using the viscous vortex particle method (VVPM), whose steady aerodynamic predictions are evaluated against experiments conducted under simulated low-density conditions. A two-stage identification strategy is adopted to reduce the number of simultaneously estimated parameters. The isolated-rotor inflow parameters are first identified and subsequently retained as fixed diagonal blocks, while the additional inter-rotor coupling parameters are identified from coaxial rotor responses. The identified model exhibits transient inflow characteristics that differ from those predicted by the conventional Pitt–Peters model and reproduces the main dynamic responses obtained from VVPM. Under the investigated Martian hovering conditions, the model yields normalized root mean square errors of 4.12% and 4.86% for the isolated-rotor and coaxial rotor identification cases, respectively. The steady thrust reductions in the upper and lower rotors are approximately 10.3% and 20.7%, respectively, relative to their corresponding isolated-rotor conditions, indicating stronger aerodynamic interference on the lower rotor. These results demonstrate the potential of the proposed identification approach to characterize asymmetric coaxial rotor inflow coupling within a reduced-order framework under the prescribed hovering conditions.