Hierarchical UAV Formation Control for Cooperative Surveillance

Sutton, Andrew J.; Fi̇dan, Barış; Walle, Dirk van der

doi:10.3182/20080706-5-kr-1001.02047

Cited by 19 publications

(9 citation statements)

References 12 publications

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“…Filters are interchangeable depending upon the purpose of UAV campaign. The flight mission is carefully planned with consideration of the position and attitude of the camera, the flight path, the image blocks, the overlaps between images, and the ground sampling distance (GSD) scale expressed in mm or cm [34]. The theoretical GSD value for the geometric resolution is given by,…”

Section: Uav Sensors and Image Acquisitionmentioning

confidence: 99%

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

et al. 2019

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Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud.

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Section: Uav Sensors and Image Acquisitionmentioning

confidence: 99%

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

et al. 2019

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“…The first agent to enter is termed A 1 , the second one A 2 , and the third one A 3 . In this case A 1 we call leader, A 2 the first follower, and A 3 an ordinary follower, using a hierarchical formation structure similar to [7]. Furthermore, we assume that the agents rotate around the target in a counter-clockwise direction.…”

Section: B Reconnaissance In the Close Vicinity Of The Targetmentioning

confidence: 98%

“…Furthermore, the speeds of the three UAVs are assumed to be constant and equal, which is typical for a fleet of three plane-type UAVs, i.e. v i (t) =v, [6], [7], draws a distinction line between the current paper and the other ones that deal with similar problems [3], [4], [8], and [9].…”

Section: A Agent Modelsmentioning

confidence: 99%

Close target reconnaissance using autonomous UAV formations

Shames

Fi̇dan

Anderson

2008

2008 47th IEEE Conference on Decision and Control

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In this paper the problem of close target reconnaissance by a formation of 3 unmanned aerial vehicles (UAVs) is considered. The overall close target reconnaissance (CTR) involves subtasks of avoiding obstacles or no-fly-zones, avoiding inter-agent collisions, reaching a close vicinity of a specified target position, and forming an equilateral triangular formation around the target. The UAVs performing the task fly at constant speeds. A decentralized control scheme is developed for this overall task considering unidirectional sensing/control architecture. Relevant analysis and simulation test results are provided.

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“…Further, a network of UASs is expected to improve the positioning accuracy (Roumeliotis and Bekey 2002) as well as aid those UASs which have lost access to the GNSS signal or are operating under GNSS challenged environments. Also a network of UASs can perform tasks such as search and rescue (Waharte et al 2009, Chung andBurdick 2008), exploration and mapping (Rekleitis et al 2003, Santamario et al), surveillance (Sutton et al 2008, Grocholsky et al 2006, Jones 2009), disaster management and situational awareness (Maza et al 2011, Kuntze et al 2012, exploration of unknown environments (Rekleitis et al 1997, Zheng et al 2005 etc. much more efficiently and much faster as compared to a single UAS.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Uas Localization Using Lowcost Sensors

Goel

Kealy

Lohani

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the individual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with individual applications, for example: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.

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Hierarchical UAV Formation Control for Cooperative Surveillance

Cited by 19 publications

References 12 publications

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Close target reconnaissance using autonomous UAV formations

Cooperative Uas Localization Using Lowcost Sensors

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