Aircraft-terrain collision avoidance is a cornerstone of aviation safety, critical to mitigating catastrophic risks posed by unintended ground contact during flight operations. Generally, existing systems rely on simplified models, deterministic assumptions, or a single predefined maneuver. These approaches often neglect dynamic uncertainties, pilot decision-making variability, and real-time computational constraints, limiting their adaptability in complex operational environments. This paper introduces an uncertainty-aware framework for terrain collision prevention that integrates high-fidelity 6-degree-of-freedom flight dynamics with probabilistic maneuver planning. The proposed system employs polynomial chaos expansion to propagate uncertainties, enabling real-time trajectory prediction and collision risk assessment. Combining uncertainty quantification, high-fidelity dynamics, and probabilistic collision detection, this work advances the robustness of autonomous terrain avoidance systems, particularly in high-stress or dynamically complex environments.
Most nonlinear filters used in spacecraft navigation are based on a linear approximation of the optimal minimum mean-square-error estimator. The unscented Kalman filter (UKF) handles nonlinear dynamics through a sigma-point transform, but the resulting state estimate remains a linear function of the measurement. This paper proposes a polynomial approximation of the Bayesian update, leading to an improved version of the classical UKF that retains the structure of the standard UKF but enriches the measurement update with higher-order (polynomial) terms. To compute the moments required by this polynomial estimator, we employ a conjugate unscented transformation (CUT), which accurately captures higher-order central moments of the state and measurement. Numerical examples, including Clohessy–Wiltshire and circular restricted three-body dynamics with non-Gaussian measurement noise, illustrate that the resulting polynomial-CUT filters improve both state estimation accuracy and covariance consistency when compared with their linear counterparts.
The handling qualities (HQ) of an H-tail flying boom directly affect the docking success rate in autonomous aerial refueling (AAR) missions. However, existing HQ criteria, established primarily based on manned operations, are difficult to apply directly to unmanned systems. To bridge this gap, this study first establishes a dynamic model of the H-tail boom in the boom-fixed coordinate system by simultaneously solving the equations of motion to eliminate universal joint constraint forces. Subsequently, representative AAR tasks for HQ assessment are devised, specifically tailored to the autonomous H-tail boom. In parallel, a comprehensive set of performance metrics is established to quantify HQ levels. Furthermore, two sets of closed-loop characteristic parameters—equivalent natural frequency and equivalent damping ratio, as well as bandwidth and phase—are proposed for HQ evaluation. Through extensive numerical simulations and quantitative evaluations, preliminary HQ level boundary recommendations are delineated across various parameter combinations. These findings provide technical references for attitude control law design, docking strategy optimization, and flight testing protocols of the autonomous H-tail refueling boom.
This paper presents a hierarchical binary tree-based framework for multiple uncrewed aerial vehicles (UAVs) to pass through a gap in a three-dimensional environment. The proposed framework uses the gap entry point and the initial UAV positions as nodes to construct a hierarchical binary tree, which provides a deterministic route for the UAVs and facilitates the identification of potential prone-to-collision nodes. A path cost function is proposed, which accounts for the crossing angles at the prone-to-collision nodes and the sum of path lengths covered by all UAVs. A genetic algorithm coupled with a greedy algorithm is utilized to obtain the hierarchical binary tree structure by minimizing the proposed path cost. Further, an occupancy zone is defined at each prone-to-collision node, and a decentralized scheduling policy assigns conflict-free time intervals to the UAVs for their entry into and exit from the occupancy zone. Numerical simulations demonstrate the effectiveness of constructing hierarchical binary trees by employing the greedy-genetic algorithm approach and validate the proposed scheduling policy in various scenarios.
Mission performance is often anchored on the fulfillment of tight attitude pointing requirements, which rely on high-precision control. Attitude transients stemming from a wide range of initial conditions or due to unpredictable dynamic events often inhibit nominal operations as pointing requirements are violated, a challenge found in the future gravitational-wave observer Laser Interferometer Space Antenna (LISA) as a consequence of micrometeoroid impacts. A suitable onboard control solution must provide the minimization of the transient peak and a fast fine-pointing recovery for a large range of initial conditions, a combination not satisfactorily achieved by existing methods. This work introduces a saturated state feedback with a time-varying gain adaptation for fast attitude convergence while maintaining a damped response. This simple and intuitive method is proven to achieve a damped response for a bounded actuation authority uncertainty. For multiple degrees of freedom constrained by a bounded actuation authority set and subject to an impulse disturbance, equal settling times are achieved in the sense that all velocity components decay simultaneously. A comparison with three methods in the time-optimality class shows the efficacy of the proposed method. The method is applied to the LISA micrometeoroid impact recovery scenario, demonstrating its performance in the presence of nonlinearities and uncertainties.
