An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty

This paper presents a sampling-based approximation for multiple unmanned aerial vehicle (UAV) task allocation under uncertainty. Our goal is to reduce the amount of calculations and improve the accuracy of the algorithm. For this purpose, Gaussian process regression models are constructed from an uncertainty parameter and task reward sample set, and this training set is iteratively refined by active learning and manifold learning. Firstly, a manifold learning method is used to screen samples, and a sparse graph is constructed to represent the distribution of all samples through a small number of samples. Then, multi-points sampling is introduced into the active learning method to obtain the training set from the sparse graph quickly and efficiently. This proposed hybrid sampling strategy could select a limited number of representative samples to construct the training set. Simulation analyses demonstrate that our sampling-based algorithm can effectively get a high-precision evaluation model of the impact of uncertain parameters on task reward.

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Distributed task allocation with critical tasks and limited capacity

Robotica ◽

10.1017/s0263574721000102 ◽

2021 ◽

pp. 1-25

Author(s):

An Zhang ◽

Mi Yang ◽

Bi Wenhao ◽

Fei Gao

Keyword(s):

Numerical Simulations ◽

Unmanned Aerial Vehicle ◽

Task Allocation ◽

Network Capacity ◽

Allocation Problem ◽

Limited Capacity ◽

Performance Impact ◽

Task List ◽

Aerial Vehicle ◽

Traditional Performance

Abstract This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

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THE EFFECT OF ACTIVE LEARNING METHOD ON STUDENTS’ ATTITUDE TOWARDS MATHEMATICS

Proceedings of International Young Scholars Workshop ◽

10.47344/iysw.v9i0.219 ◽

2020 ◽

Vol 9 ◽

Author(s):

Bibigul Kazmagambet ◽

Zhansaya Ibraimova ◽

Serkan Kaymak

Keyword(s):

Active Learning ◽

Learning Strategy ◽

Teaching Strategy ◽

Learning Method ◽

Learning Group ◽

Attitude Towards Mathematics ◽

Active Learning Method ◽

The Republic ◽

Active Learning Strategy ◽

The Impact

The world is changing so fast, and therefore education needs to adapt to the challenges of times. In order to update the content of school education in the Republic of Kazakhstan modern trends are going to be used. These trends contain pedagogical methods that can be used to preserve and even increase internal motivation, as active learning. Active learning method is an treatment where students participate or interact with the learning process, as opposed to passively taking in the information.The goal of this study is to identify the impact of active learning method on 10th grade students’ attitude towards mathematics of the students the second semester of the school year 2019-2020. More specifically, it attempted to determine and compare the attitude toward mathematics of students’ exposure to active learning and traditional teaching strategy. The Likert scale used to evaluate the attitude of students toward mathematics. Mean, Cronbach value, T-test were the statistical tools used in anatomizing and interpreting the research data. The discovering showed that the students in the active learning group had auspicious attitude than students in the conventional teaching group. According to the findings after research, we saw the direct relation between attitude and active learning. It is concluded that the students’ attitude toward mathematics was better by using active learning strategy. It is recommended that mathematics teacher should use active learning strategy in order to improve the attitude toward mathematics of the students.Keywords: attitude, mathematics, active learning

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Dynamic Stability and Control of a Manipulating Unmanned Aerial Vehicle

International Journal of Aerospace Engineering ◽

10.1155/2018/3481328 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Yunping Liu ◽

Xijie Huang ◽

Yonghong Zhang ◽

Yukang Zhou

Keyword(s):

Stability Analysis ◽

Lyapunov Exponent ◽

Dynamic Stability ◽

Unmanned Aerial Vehicle ◽

Impedance Control ◽

System Stability ◽

Constructive Method ◽

Aerial Vehicle ◽

And Control ◽

The Impact

This paper focuses on the dynamic stability analysis of a manipulator mounted on a quadrotor unmanned aerial vehicle, namely, a manipulating unmanned aerial vehicle (MUAV). Manipulator movements and environments interaction will extremely affect the dynamic stability of the MUAV system. So the dynamic stability analysis of the MUAV system is of paramount importance for safety and satisfactory performance. However, the applications of Lyapunov’s stability theory to the MUAV system have been extremely limited, due to the lack of a constructive method available for deriving a Lyapunov function. Thus, Lyapunov exponent method and impedance control are introduced, and the Lyapunov exponent method can establish the quantitative relationships between the manipulator movements and the dynamics stability, while impedance control can reduce the impact of environmental interaction on system stability. Numerical simulation results have demonstrated the effectiveness of the proposed method.

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Morphological Band Registration of Multispectral Cameras for Water Quality Analysis with Unmanned Aerial Vehicle

Remote Sensing ◽

10.3390/rs12122024 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2024 ◽

Cited By ~ 1

Author(s):

Wonkook Kim ◽

Sunghun Jung ◽

Yongseon Moon ◽

Stephen C. Mangum

Keyword(s):

Unmanned Aerial Vehicle ◽

Red Tide ◽

Spatial Scales ◽

Quality Analysis ◽

Geometric Transformation ◽

Concentration Estimation ◽

Individual Bands ◽

Aerial Vehicle ◽

Short Time ◽

The Impact

Multispectral imagery contains abundant spectral information on terrestrial and oceanic targets, and retrieval of the geophysical variables of the targets is possible when the radiometric integrity of the data is secured. Multispectral cameras typically require the registration of individual band images because their lens locations for individual bands are often displaced from each other, thereby generating images of different viewing angles. Although this type of displacement can be corrected through a geometric transformation of the image coordinates, a mismatch or misregistration between the bands still remains, owing to the image acquisition timing that differs by bands. Even a short time difference is critical for the image quality of fast-moving targets, such as water surfaces, and this type of deformation cannot be compensated for with a geometric transformation between the bands. This study proposes a novel morphological band registration technique, based on the quantile matching method, for which the correspondence between the pixels of different bands is not sought by their geometric relationship, but by the radiometric distribution constructed in the vicinity of the pixel. In this study, a Micasense Rededge-M camera was operated on an unmanned aerial vehicle and multispectral images of coastal areas were acquired at various altitudes to examine the performance of the proposed method for different spatial scales. To assess the impact of the correction on a geophysical variable, the performance of the proposed method was evaluated for the chlorophyll-a concentration estimation. The results showed that the proposed method successfully removed the noisy spatial pattern caused by misregistration while maintaining the original spatial resolution for both homogeneous scenes and an episodic scene with a red tide outbreak.

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Distributed cooperative control for deployment and task allocation of unmanned aerial vehicle networks

IET Control Theory and Applications ◽

10.1049/iet-cta.2012.0779 ◽

2013 ◽

Vol 7 (11) ◽

pp. 1574-1582 ◽

Cited By ~ 28

Author(s):

Jinwen Hu ◽

Zhao Xu

Keyword(s):

Unmanned Aerial Vehicle ◽

Task Allocation ◽

Cooperative Control ◽

Vehicle Networks ◽

Aerial Vehicle ◽

Distributed Cooperative Control ◽

And Task

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TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b1-917-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 917-923

Author(s):

T. Lendzioch ◽

J. Langhammer ◽

M. Jenicek

Keyword(s):

Unmanned Aerial Vehicle ◽

Snow Depth ◽

Forest Canopy ◽

Forest Succession ◽

Coniferous Forest ◽

Snow Accumulation ◽

Digital Photogrammetry ◽

Area Index ◽

Aerial Vehicle ◽

The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

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