Historically, in the initial stages of solid rocket motor (SRM) development, performance parameters, such as specific impulse, total impulse, mass, and thrust, have been prioritized, with cost considerations often treated as secondary. Consequently, SRM performance optimization under cost constraints has emerged as a central objective in aerospace propulsion. To address this gap, this study establishes a cost–performance evaluation model for SRMs. A Kriging surrogate model, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are leveraged to minimize the manufacturing cost and maximize the terminal velocity of SRM engines, subject to constraints associated with the maximum operating pressure of the combustion chamber and burn time. First, a cost–performance calculation model for an SRM is developed and validated. Subsequently, Pearson correlation analysis and Sobol-based global sensitivity analysis are combined to reduce the dimensionality of the design parameters, and optimal Latin hypercube sampling is used to generate the training samples. Building on this foundation, a Kriging surrogate model is constructed. The cost–performance model of the SRM is subjected to multi-objective optimization using NSGA-II and TOPSIS to support decision-making. The results indicate that the proposed cost–performance calculation model achieves an error below 5%, demonstrating high accuracy. Among the design parameters, the combustion chamber length, nozzle outlet area, and expansion ratio significantly influence the cost and performance of SRMs. The surrogate models exhibit strong predictive accuracy, with coefficients of determination exceeding 0.9. The optimized TOPSIS scheme yields a performance improvement of 10.94% with a cost increase of 4.15% compared with the reference scheme. In summary, the cost–performance evaluation and optimization framework established in this work provides quantitative decision support for SRM design under cost constraints, and the integrated methodology can be extended to other aerospace propulsion systems or complex engineering equipment. This contributes to achieving synergistic optimization of performance and cost under resource limitations, and offers practical guidance for advancing affordability-driven design in propulsion engineering.

In-flight wingtip docking can connect multiple UAVs into a high-aspect-ratio chained-wing configuration, offering potential improvements in aerodynamic efficiency, endurance, and cruise performance. Reliable close-range 6-DoF relative pose estimation is essential for precise docking; however, existing studies have focused primarily on aerodynamic characteristics, docking mechanisms, and guidance and control, while robust monocular pose estimation under partial occlusion and image degradation remains insufficiently investigated. To address this gap, a cooperative-target-based monocular vision method is proposed for six-degree-of-freedom relative pose estimation during in-flight wingtip docking in a chained-wing UAV system. An asymmetric seven-ring planar cooperative target is designed to reduce feature-identification ambiguity and retain sufficient geometric constraints under partial occlusion. The front end combines YOLOv8n-seg instance segmentation with local inner–outer ring refinement and topology-based feature classification. According to target visibility, the back end adaptively selects seven-, five-, or four-point pose-estimation modes, refines valid solutions by minimizing reprojection error, and removes isolated suspicious candidates when necessary. The method is evaluated on a controlled indoor hardware-in-the-loop platform as a pre-flight assessment of its incremental measurement performance. In controlled incremental-response experiments, the mean absolute adjacent-increment errors do not exceed $0.08\mathrm{mm}$ in translation and $0.13°$ in rotation. The complete method achieves a pose-solving success rate of 99.07%. Experiments involving progressive wing occlusion and synthetic directional motion blur further demonstrate that the method can provide stable and continuous relative pose output under challenging observation conditions.

To address the strong coupling among engine geometry, propulsion performance, aerodynamic response, flight trajectory, and manufacturing cost, this study establishes an integrated propulsion–aerodynamics–trajectory–cost design framework for high-speed, long-range rocket-boosted vehicles. Six chamber and nozzle geometric parameters are selected as design variables. An engine performance model is first used to calculate propulsion responses, including thrust, chamber pressure, and specific impulse. A mass and configuration update model then transfers the effects of engine parameter variations to the overall vehicle characteristics, while aerodynamic data and a two-dimensional point-mass trajectory model are introduced to obtain mission-level indicators, including maximum velocity, maximum altitude, and range. An existing manufacturing cost decomposition model for solid rocket motors is extended by coupling the cost response with component masses, geometric dimensions, and parameter-update relationships, thereby enabling the simultaneous evaluation of mission performance and manufacturing cost within the integrated computational chain. Kriging surrogate models are constructed for rapid prediction of the coupled system responses, entropy-weighted TOPSIS is used to screen feasible candidates, and SQP is employed for continuous constrained refinement. Compared with the baseline design, the comprehensive evaluation index increases from 0.4861 to 0.6110. The maximum velocity, maximum altitude, and range increase by 8.29%, 26.81%, and 21.24%, respectively, while the manufacturing cost increases by only 0.48%. The evaluation index is also 15.68% higher than that of the engine-level optimized design. These results demonstrate that the integrated consideration of propulsion, aerodynamics, trajectory, and manufacturing cost improves mission-level performance–cost trade-offs and provides a system-level design approach for mission-oriented and cost-aware solid rocket motor development.