Journal of Aerospace Information Systems

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ISSN / EISSN : 1940-3151 / 2327-3097
Total articles ≅ 502
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Guangrui Xie,
Journal of Aerospace Information Systems pp 1-11;

As an important type of dynamic data-driven application system, unmanned aerial vehicles (UAVs) are widely used for civilian, commercial, and military applications across the globe. An increasing research effort has been devoted to trajectory prediction for non-cooperative UAVs to facilitate their collision avoidance and trajectory planning. Existing methods for UAV trajectory prediction typically suffer from two major drawbacks: inadequate uncertainty quantification of the impact of external factors (e.g., wind) and inability to perform online detection of abrupt flying pattern changes. This paper proposes a Gaussian process regression (GPR)-based trajectory prediction framework for UAVs featuring three novel components: 1) GPR with uniform confidence bounds for simultaneous predictive uncertainty quantification, 2) online trajectory change-point detection, and 3) adaptive training data pruning. The paper also demonstrates the superiority of the proposed framework to competing trajectory prediction methods via numerical studies using both simulation and real-world datasets.
Hyungjoo Ahn, Junwoo Park, Hyochoong Bang, Yoonsoo Kim
Journal of Aerospace Information Systems pp 1-15;

This study proposes a collision-free motion planning framework for indoor autonomous flight of multirotor unmanned aerial vehicle (UAV) based on the convex model predictive control (MPC) approach under a three-dimensional point cloud environment. The suggested framework is divided into three steps: full reference path generation, piecewise flight corridor (PFC) creation, and MPC-based motion planning. The framework begins with reconstructing boundary surfaces that can encapsulate the given point cloud in order to generate a full reference path by applying Dijkstra and Voronoi diagram algorithms. Then PFC that represents locally convex and feasible flight corridor is generated using the current vehicle state, triangulized obstacle, and full reference path. In such a way, the entire problem breaks down into a series of discretized convex motion planning problems whose solution can be found by applying MPC iteratively until the UAV reaches its final destination. The constraints of the MPC are set up with the dynamics of the UAV, PFC, and the performance limitation of the platform. The framework is verified with simulation under a MATLAB environment. As a result, the UAV can find the control variable needed to reach the final destination with the suggested framework. Also, the computational time of the suggested framework is shorter than those of full reference path optimization methods.
, Changho Lee, Youngmin Park, Matthew B. Rhudy, Dongjin Lee, Dongjin Jang, Wonkeun Youn
Journal of Aerospace Information Systems pp 1-13;

Identifying the drag parameters of unmanned aerial vehicles (UAVs) is crucial for guaranteeing their aerodynamic efficiency. However, in contrast to commercial aircraft, obtaining accurate UAV drag parameters is challenging because the existing approaches rely on analytical models or require accurate modeling of the engine thrust, which highly depends on time-varying wind conditions. To address this challenge, this paper first proposes a novel in-flight estimation algorithm for the air data (airspeed, angle of attack, and sideslip angle) and drag parameters of UAVs. With this approach, there is no need to compute all of the contributing components for drag, to model the thrust of the UAVs, or to perform complicated wind tunnel testing/computational fluid dynamics analysis to obtain the drag parameter. Instead, the proposed algorithm requires only standard sensors such as inertial measurement units and air data systems during gliding flight. Then, an efficient glide phase detection algorithm for initiating the filter is proposed. Simulation and experimental flight testing of a UAV demonstrate that the proposed algorithm yields accurate zero lift drag coefficient, attitude, and air data estimation results according to thorough validation with newly derived metrics for performance evaluation.
Aneesh M. Heintz, Mason Peck, Fangchen Sun, Ian Mackey
Journal of Aerospace Information Systems pp 1-12;

Neural networks have become state-of-the-art computer vision tools for tasks that learn implicit representations of geometrical scenes. This paper proposes a two-part network architecture that exploits a view-synthesis network to understand a context scene and a graph convolutional network to generate a shape body model of an object within the field of view of a spacecraft’s optical navigation sensors. Once the first part of the network’s architecture understands the spacecraft’s environment, it can generate images from novel observations. The second part uses a multiview set of images to construct a 3D graph-based representation of the object. The proposed network pipeline produces shape models with accuracies that compete with state-of-the-art methods currently used for missions to small bodies. The network pipeline can be trained for multi-environment missions. Moreover, the onboard implementation may be more cost-effective than the current state-of-the-art.
Chuanqi Zhang, Yunfeng Cao, Meng Ding, Xu Li
Journal of Aerospace Information Systems pp 1-10;

The flight safety of low-altitude small fixed-wing unmanned aerial vehicles (UAVs) is often threatened by obstacles such as buildings. This requires UAVs to have the ability to autonomously measure the depth of objects ahead. However, existing depth measurement methods based on multiview geometry and handcrafted features still have problems in accuracy and scene suitability. This paper proposes an object depth measurement and filtering method for UAVs by using monocular images. Firstly, the length of the line segment between feature points instead of the pixel position of feature point is used to solve object depth, which reduces the adverse effect of feature matching error. Meanwhile, in order to adapt to UAV platforms, height and attitude changes are both considered in the modeling process. Moreover, the sequence of object depth values corresponding to the image sequence is filtered by an extended Kalman filter to reduce oscillations. The effectiveness of the whole scheme is verified by visual simulation. Results show that the proposed method achieves better accuracy than other depth measurement methods based on multiview geometry.
Toshikazu Motoda
Journal of Aerospace Information Systems pp 1-11;

Monte Carlo simulation (MCS) has been widely used in the development of aerospace vehicles. The rapid growth of computer power has allowed it to be applied in particular to the evaluation of systems before flight, where its capability of directly evaluating nonlinear systems incorporating various uncertain inputs is a key advantage. To improve a system’s design, it is crucial to detect those uncertain inputs that have a significant influence on unsatisfactory MCS results. This paper presents a new approach for detecting such input parameters quickly. It combines two statistical tests: Kuiper’s test and the Z test. The advantage of the approach is that it uses only input and output data from MCS simulation evaluations: knowledge of the system model or further simulations are unnecessary. Because its results can be obtained quickly, the method contributes to the efficient development of flight vehicles. The approach also can be applied to MCS results that have a relatively small number of unsatisfactory results, so it is effective not only during the early stages of design where failures are more likely, but also for successive design iterations where failures are fewer.
, Ludovic Apvrille, Rob Vingerhoeds
Journal of Aerospace Information Systems pp 1-13;

Systems engineering, or engineering in general, has long been relying on document-centric approaches. Switching to model-based systems engineering, or MBSE for short, has extensively been discussed over the past three decades. Since about two decades, MBSE has been commonly associated with the modeling language SysML (Systems Modeling Language), which offers a standardized notation, not a methodology of using it. SysML needs therefore to be associated with a methodology supported by tools. In this paper, a methodology supported by the free and open-source software TTool is associated with SysML. This paper focuses discussion on methodological issues, leading the authors to share their experience in real-time systems modeling. Modeling with SysML is more than just drawing the different diagrams. Associated tools offer possibilities to analyze SysML models for specific properties. In this paper, verification addresses both safety and security properties. The TTool model checker inputs the SysML model enriched with safety properties to be verified and outputs an yes/no answer for each property. Security verification checks SysML models against confidentiality, integrity, and authenticity properties. As an illustration of the proposed approach, an aircraft cockpit door control system is modeled in SysML and verified against safety and security properties.
Haichao Li, Linwei Qiu, Zhi Li, Bo Meng, Jianbin Huang, Zhimin Zhang
Journal of Aerospace Information Systems, Volume 18, pp 755-761;

Accurate detection and segmentation of obstacles is the key to the smooth operation of the planetary rovers and the basic guarantee of scientific exploration mission. The traditional method of rock segmentation based on boundary detector is affected by the change of illumination and dust storms. To address this problem, this paper proposes an improved U-net-based architecture combined with Visual Geometry Group (VGG) and dilated convolutional neural network for the segmentation of rocks from images of planetary exploration rovers. The proposed method also has a contracting path and an expansive path to get high-resolution output similar with U-Net. In the contracting path, the convolution layers in U-Net are replaced by the convolutional layers of VGG16. Inspired by the dilated convolution, the multiscale dilated convolution in the expansive path is proposed. Furthermore, our method is further optimized in the expansive path. To evaluate the proposed method, extensive experiments on segmentation with the Mars dataset have been conducted. The experimental results demonstrate that the proposed method produces accurate semantic segmentation and identification results automatically and outperforms state-of-the-art methods.
Hyeok-Joo Chae, Han-Lim Choi
Journal of Aerospace Information Systems, Volume 18, pp 790-802;

Although deep learning techniques have been successfully implemented to solve domain-specific unmanned aerial vehicle planning problems, it is still a challenging task to develop a learning method to solve multidomain planning problems. Because the multidomain problems often involve learning more parameters, a dilated dataset diminishes learning speed due to its size and high dimensionality. The following two observations help tackle the issue: the state space of planning problems can be decomposed into representations of the domain state and system state, and the dimensionality problem often arises due to the huge size of the domain rather than system state. Inspired by such observations, this work presents a learning framework consisting of two networks: 1) a domain abstraction network in the form of a variational autoencoder that reduces the dimension of the domain space into a compact form, and 2) a planning network that generates a planning solution for a given domain setting. The effectiveness of the proposed learning framework is validated in case studies.
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