Sequential Convex Trajectory Planning for Morphing Vehicles Based on No-Fly Zone Threat Measure Modeling

To address the difficulty that cross-domain morphing vehicles are prone to infeasibility in complex environments with dense hard-constraint no-fly zones, a three-dimensional no-fly zone modeling approach based on a soft-constraint normalized threat measure is proposed and integrated into sequential convex programming for numerical solution. Based on the normalized distance from the vehicle to the center of spherical/ellipsoidal no-fly zones, an instantaneous normalized threat measure model is constructed, and the overall threat exposure time (OTET) is defined through time integration. This relaxes the traditional hard no-fly zone constraints into soft constraints expressed by the normalized threat measure, which can be embedded into the optimization objective. Then, following the idea of alternating optimality–feasibility iteration, the soft-constraint model is embedded into the sequential convex programming framework. In the optimality iteration, a convex subproblem without relaxation is solved to obtain a descent direction of the objective; in the feasibility iteration, slack variables are introduced and the penalty coefficients are updated adaptively using dual variables. This achieves integrated optimization of the morphing vehicle’s trajectory and configuration. Taking a variable-sweep vehicle as the research object, two operating conditions—adaptive morphing and fixed high-lift-to-drag ratio configuration—are set and compared in scenarios with multiple ellipsoidal/elliptical–cylindrical no-fly zones. Under the baseline scenario, the adaptive morphing strategy achieves an OTET of 1.56 s, which is 96.2% lower than the 40.85 s of the fixed configuration. A sensitivity analysis with elliptical–cylindrical no-fly zones further shows that the adaptive morphing strategy maintains a significant advantage, with an OTET of 10.02 s compared with 74.75 s for the fixed configuration. All state and control variables, as well as path constraints such as heat flux, dynamic pressure, and load factor, strictly satisfy the prescribed bounds. Checked by high-accuracy dynamic integration, the terminal state errors of the optimized solution remain within the allowable range, indicating the continuous feasibility of the optimization solution. The proposed method significantly enhances the mission adaptability of cross-domain morphing vehicles in no-fly zone environments while ensuring solution accuracy, and the proposed threat assessment method can provide a theoretical reference for morphing intelligent decision-making.