Automated Aerial Docking System Using Onboard Vision-Based Deep Learning

Choi, Andrew Jaeyong; Park, Jeong Hwan; Han, Jae‐Hung

doi:10.2514/1.i011053

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…Nonetheless, these measurement systems have some shortcomings: they are susceptible to unstable environmental factors, such as temperature, humidity, and air disturbances, especially during station transfers. Additionally, line-of-sight obstruction and lighting are significant problems encountered during vision measurement [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Construction of a measurement system and analytical solution for docking pose of large aircraft components based on draw-wire displacement sensors

Sun,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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Relative pose detection is a key technology in large component automatic docking systems. In this paper, a measurement system has been developed to detect the relative pose of large aircraft components with draw-wire displacement sensors, while an analytical calculation method has been proposed to determine the docking pose of large aircraft components. This method establishes a measurement model for the docking pose of large components and separates the position and orientation solutions based on the distances between seven sets of measurement points measured by the draw-wire displacement sensors. First, the principle of three-dimensional rendezvous positioning is refined to ascertain the relative position of the components. Then, by analyzing the properties of the rotation matrix and the coupling relationship between the position and orientation variables of the movable component, 15 compatible equations containing only two variables with a maximum degree of four are obtained. Based on this set of equations, a unique solution for the rotation matrix was obtained through the variable substitution method, gradually eliminating higher-order terms. Finally, the correctness of the method is verified through numerical examples. Unlike typical parallel mechanism models, this model can directly obtain 15 compatible equations without the need for methods such as Gröbner bases. Moreover, compared to the measurement method using six draw-wire displacement sensors, a unique solution can be directly determined, solving the problem of multiple solution selection. This measurement method only requires measuring the distance between measurement points to solve the relative pose, with low equipment costs and less susceptibility to external environmental factors.

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Section: Introductionmentioning

confidence: 99%

Construction of a measurement system and analytical solution for docking pose of large aircraft components based on draw-wire displacement sensors

Sun,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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“…The recent advances in unmanned aerial vehicles (UAVs) and computing technologies (i.e., artificial intelligence, embedded systems, soft computing, cloud and edge computing, sensor fusion, etc.) have expanded the potential and extended the capabilities of UAVs [ 3 , 4 ]. One of the key abilities required for autonomous flight systems is the methodology of mid-air collision avoidance [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System

Park¹,

Choi

2023

Sensors

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The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced.

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“…• Susceptible to unstable environmental factors. For instance, laser trackers are susceptible to factors such as temperature, humidity, and air disturbances, especially at transfer stations, and the impact of line-of-sight obstructions and light sources is a major issue during visual measurements [3]- [8]. In order to improve the efficiency of assembly docking and reduce the cost while satisfying the docking accuracy of large component assembly, a method for measuring the docking pose of large components based on a draw-wire displacement sensor is proposed.…”

Section: Introductionmentioning

confidence: 99%

Docking Pose Measurement Method for Large Components Based on Draw-Wire Displacement Sensors

Sun,

Wang,

Tian

et al. 2023

Measurement Science Review

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A method for measuring the docking pose of large components based on the draw-wire displacement sensor is proposed. In this method, coordinate systems and measurement points are established on the docking surfaces of fixed and moving components. The draw-wire displacement sensor is used to measure the distances between these measurement points. A mathematical model based on the distances between the measurement points is established, and the three-sphere rendezvous positioning principle is optimized to obtain the spatial positions of the measurement points. Consequently, the pose deviations of the fixed and moving components in all six degrees of freedom (6DOF) are determined. A simulation analysis of the measurement uncertainty of the obtained pose deviations is performed, resulting in a composite standard uncertainty obtained from the measurement standard uncertainties of different sensors. The simulation results show that the composite standard uncertainty is most affected in the x-axis translation direction and least affected in the x-axis rotation direction. With this method, only the distances between the measurement points need to be measured to determine the corresponding pose relationships. The cost of the equipment is low, and it is not easily affected by external factors such as the environment.

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Automated Aerial Docking System Using Onboard Vision-Based Deep Learning

Cited by 13 publications

References 21 publications

Construction of a measurement system and analytical solution for docking pose of large aircraft components based on draw-wire displacement sensors

Construction of a measurement system and analytical solution for docking pose of large aircraft components based on draw-wire displacement sensors

Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System

Docking Pose Measurement Method for Large Components Based on Draw-Wire Displacement Sensors

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