Aerospace

Free-floating space manipulators exhibit strong dynamic coupling between the robotic system and its spacecraft base, where configuration transitions may generate significant base-attitude disturbances. This paper proposes a constrained trajectory-planning framework using eighth-order Bézier curves, in which boundary conditions of position, velocity, and acceleration are analytically satisfied through endpoint control points, while intermediate control points are optimized under joint constraints. To solve the resulting nonlinear constrained optimization problem, a Position–Velocity Hierarchical Command Cone Search Optimizer (PV-HCCSO) is developed. The proposed optimizer integrates a hierarchical search architecture, representative-guided multi-region exploration, adaptive cone search, position–velocity cooperation, and stagnation-aware diversity-restoration mechanisms. Numerical simulations of a 7-DOF free-floating space manipulator demonstrate that the generated trajectories satisfy all kinematic constraints while effectively suppressing terminal base-attitude disturbances. Compared with WCA, PSO, and CPO, PV-HCCSO achieves reductions of 93.89%, 85.31%, and 81.96% in the mean final fitness value, respectively. Statistical analysis over 40 independent trials confirms significant performance improvements (F = 33.705, p < 0.001). Furthermore, evaluations on the 20-dimensional CEC2022 benchmark suite show superior optimization capability with an average rank of 1.28. The results demonstrate the effectiveness and robustness of PV-HCCSO for constrained trajectory planning of free-floating space manipulators.

The flowpath configuration of a scramjet combustor decisively governs its combustion performance. Scramjet combustors are generally classified into rectangular and circular cross-section types. For circular cross-section combustors, numerous flowpath configurations have been proposed worldwide, accompanied by extensive numerical simulations and experimental investigations. This paper systematically reviews proper flowpath configurations for circular cross-section scramjet combustors. It first discusses wall configurations, then examines cavity flameholders, strut flameholders, and other flowpath arrangements. Key geometrical parameters, including wall shape, cavity geometry, strut layout, and their coupling effects, are considered. The influences of these configurations on combustor performance are briefly analyzed, with particular attention to fuel mixing, flame stabilization, and combustion efficiency. Finally, the current research status of circular scramjet combustor geometric design is summarized, and future development prospects are outlined. By consolidating existing knowledge and identifying remaining challenges, this review aims to provide a useful reference for the optimal design of circular cross-section scramjet combustors and to support further advances in high-performance propulsion systems.

In multi-hop UAV ad hoc networks, three-dimensional high mobility, strong line-of-sight interference and a hard saturation constraint on control-plane broadcasting distinguish the channel load estimation problem from its terrestrial counterpart: the HELLO broadcast interval cannot be shortened arbitrarily, so the channel occupancy statistics (COS) required by the statistical priority-based multiple access (SPMA) protocol must be inferred from observations that are asynchronous and separated by long idle gaps. The existing SMA-KF filtering method adopts a first-order random-walk prediction model and lacks trend-tracking capability, and thus easily produces systematic lag when the channel load changes with a trend; directly introducing data-driven compensation, in turn, brings the risk of closed-loop estimation divergence under long gaps. To this end, this paper proposes a residual-corrected variable-interval Kalman filtering (RC-VKF) method for channel load estimation. The method builds a closed-loop recursive framework with the variable-interval Kalman filter while retaining the capability to quantify state uncertainty; it constructs residual labels only from the node’s local packet observations and adopts recursive least squares to perform incremental online training of the corrector, without any offline ground-truth dataset; it designs a confidence gating mechanism driven by the gap duration, under which the correction term gradually withdraws when the gap is extra long and the algorithm automatically degenerates into a covariance-bounded Kalman filter, thereby guaranteeing the stability of the closed-loop estimation at the architectural level. Closed-loop simulation results of a multi-hop network show that, compared with SMA-KF, the proposed RC-VKF method achieves a consistent improvement in estimation accuracy under different traffic intensities and HELLO broadcast periods; meanwhile, it can effectively suppress the large estimation errors in the 95th-percentile tail and improve the reliability of the SPMA transmission decision; when observations become extremely sparse, the correction term is withdrawn by the confidence gate and the estimator degrades gracefully to the filtering kernel; within the bounded gap range assumed in the stability analysis, its accuracy is essentially that of the filtering kernel, and beyond that range, the estimate reduces to the hold-based prediction instead of diverging.