The Experts below are selected from a list of 4224 Experts worldwide ranked by ideXlab platform
Jan Faigl - One of the best experts on this subject based on the ideXlab platform.
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MESAS - Aerial Reconnaissance and Ground Robot Terrain Learning in Traversal Cost Assessment
Modelling and Simulation for Autonomous Systems, 2020Co-Authors: Milos Pragr, Petr Vaňa, Jan FaiglAbstract:In this paper, we report on the developed system for assessment of ground unit terrain traversal cost using Aerial Reconnaissance of the expected mission environment. The system combines an Aerial vehicle with ground robot terrain learning in the traversal cost modeling utilized in the mission planning for ground units. The Aerial vehicle is deployed to capture visual data used to build a terrain model that is then used for the extraction of the terrain features of the expected operational area of the ground units. Based on the previous traversal experience of the ground units in similar environments, the learned model of the traversal cost is employed to predict the traversal cost of the new expected operational area to plan a cost-efficient path to visit the desired locations of interest. The particular modules of the system are demonstrated in an experimental scenario combining the deployment of an unmanned Aerial vehicle with a multi-legged walking robot used for learning the traversal cost model.
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Aerial Reconnaissance and ground robot terrain learning in traversal cost assessment
international conference on Modelling and simulation, 2019Co-Authors: Milos Pragr, Petr Vaňa, Jan FaiglAbstract:In this paper, we report on the developed system for assessment of ground unit terrain traversal cost using Aerial Reconnaissance of the expected mission environment. The system combines an Aerial vehicle with ground robot terrain learning in the traversal cost modeling utilized in the mission planning for ground units. The Aerial vehicle is deployed to capture visual data used to build a terrain model that is then used for the extraction of the terrain features of the expected operational area of the ground units. Based on the previous traversal experience of the ground units in similar environments, the learned model of the traversal cost is employed to predict the traversal cost of the new expected operational area to plan a cost-efficient path to visit the desired locations of interest. The particular modules of the system are demonstrated in an experimental scenario combining the deployment of an unmanned Aerial vehicle with a multi-legged walking robot used for learning the traversal cost model.
Qu Guo-zhi - One of the best experts on this subject based on the ideXlab platform.
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Fast mosaic method of Aerial Reconnaissance images using affine transform and cross-correlation
Journal of Optoelectronics·laser, 2011Co-Authors: Qu Guo-zhiAbstract:A fast image mosaic method is proposed for an Aerial Reconnaissance CCD camera.In this method,parameters of affine transformation were first evaluated by using the work parameters of the camera,which can supply a rough registration area of images.Then,the image cross-correlation was employed to obtain a higher precision in the affine transformation parameters by searching this rough area.Finally,a bilinear interpolation method was used to fuse the overlapping area of images.Experimental results of this method prove that it can reduce the processing time effectively,abate the requirements for image processing hardware,and prevent the wrong matching caused by only using cross-correlation method.Images generated by the method can meet the requirements of camera systems.
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Design of super-wide-angle Aerial Reconnaissance CCD camera
Infrared and Laser Engineering, 2010Co-Authors: Qu Guo-zhiAbstract:A new design scheme of super-wide-angle Aerial Reconnaissance CCD camera is described,which is composed of an optical drum and a reflecting mirror rotation mechanism.The optical drum is installed optically parallel to the aircraft velocity,and the reflecting mirror is positioned inside of the optical drum.Partial images are taken during the optical drum rotation,and image motion caused by the roll and the pitch attitude disturbance were compensated by the optical drum and the mirror rotation mechanism,respectively.Unlike the traditional CCD camera,this camera possesses characteristics of small body size,super-wide-angle and high resolution images with adjustable image overlap rates,and it does not require any other compensation mechanism.Finally,there-channel images taken during flight test are presented to demonstrate the effectiveness of the designed camera.The result shows that the scheme meets Aerial Reconnaissance with wide view field and high resoultion.
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Calculating Attitude Disturbance Image Motion of One Aerial Reconnaissance CCD Camera
Opto-electronic Engineering, 2010Co-Authors: Qu Guo-zhiAbstract:According to the operating principle of one scanning Aerial Reconnaissance CCD camera, an analysis expression of image motion was derived with collinearity equation, which was about attitude disturbance and image obliquity. Combining the working parameters of imaging obliquity and attitude disturbance, a simulation analysis was given, and the results show that image motion is independent on image obliquity and linearizes relationship of attitude disturbance when image obliquity θ0.13 rad. Beyond this range, image motion is nonlinear with image obliquity and attitude disturbance. The whole trends will increase coupling with the increase of the disturbance, which can supply a theory basis for the same species of camera and its image motion compensation devices.
William B Carlton - One of the best experts on this subject based on the ideXlab platform.
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reactive tabu search in unmanned Aerial Reconnaissance simulations
Winter Simulation Conference, 1998Co-Authors: Joel L Ryan, Glenn T Bailey, James T Moore, William B CarltonAbstract:We apply a Reactive Tabu Search (RTS) heuristic within a discrete-event simulation to solve routing problems for unmanned Aerial vehicles (UAVs). Our formulation represents this problem as a multiple traveling salesman problem with time windows (mTSPTW), with the objective of maximizing expected target coverage. Incorporating weather and probability of UAV survival at each target as random inputs, the RTS heuristic in the simulation searches for the best solution in each realization of the problem scenario in order to identify those routes that are robust to variations in weather, threat, or target service times. We present an object-oriented implementation of this approach using CACI's simulation language MODSIM.
Milos Pragr - One of the best experts on this subject based on the ideXlab platform.
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MESAS - Aerial Reconnaissance and Ground Robot Terrain Learning in Traversal Cost Assessment
Modelling and Simulation for Autonomous Systems, 2020Co-Authors: Milos Pragr, Petr Vaňa, Jan FaiglAbstract:In this paper, we report on the developed system for assessment of ground unit terrain traversal cost using Aerial Reconnaissance of the expected mission environment. The system combines an Aerial vehicle with ground robot terrain learning in the traversal cost modeling utilized in the mission planning for ground units. The Aerial vehicle is deployed to capture visual data used to build a terrain model that is then used for the extraction of the terrain features of the expected operational area of the ground units. Based on the previous traversal experience of the ground units in similar environments, the learned model of the traversal cost is employed to predict the traversal cost of the new expected operational area to plan a cost-efficient path to visit the desired locations of interest. The particular modules of the system are demonstrated in an experimental scenario combining the deployment of an unmanned Aerial vehicle with a multi-legged walking robot used for learning the traversal cost model.
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Aerial Reconnaissance and ground robot terrain learning in traversal cost assessment
international conference on Modelling and simulation, 2019Co-Authors: Milos Pragr, Petr Vaňa, Jan FaiglAbstract:In this paper, we report on the developed system for assessment of ground unit terrain traversal cost using Aerial Reconnaissance of the expected mission environment. The system combines an Aerial vehicle with ground robot terrain learning in the traversal cost modeling utilized in the mission planning for ground units. The Aerial vehicle is deployed to capture visual data used to build a terrain model that is then used for the extraction of the terrain features of the expected operational area of the ground units. Based on the previous traversal experience of the ground units in similar environments, the learned model of the traversal cost is employed to predict the traversal cost of the new expected operational area to plan a cost-efficient path to visit the desired locations of interest. The particular modules of the system are demonstrated in an experimental scenario combining the deployment of an unmanned Aerial vehicle with a multi-legged walking robot used for learning the traversal cost model.
Petr Stodola - One of the best experts on this subject based on the ideXlab platform.
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Trajectory Optimization in a Cooperative Aerial Reconnaissance Model
Sensors, 2019Co-Authors: Petr Stodola, Jan Drozd, Jan Nohel, Jan Hodický, Dalibor ProcházkaAbstract:In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as Reconnaissance and surveillance. This article examines a model for planning Aerial Reconnaissance using a fleet of mutually cooperating unmanned Aerial vehicles to increase the effectiveness of the task. The model deploys a number of waypoints such that, when every waypoint is visited by any vehicle in the fleet, the area of interest is fully explored. The deployment of waypoints must meet the conditions arising from the technical parameters of the sensory systems used and tactical requirements of the task at hand. This paper proposes an improvement of the model by optimizing the number and position of waypoints deployed in the area of interest, the effect of which is to improve the trajectories of individual unmanned systems, and thus increase the efficiency of the operation. To achieve this optimization, a modified simulated annealing algorithm is proposed. The improvement of the model is verified by several experiments. Two sets of benchmark problems were designed: (a) benchmark problems for verifying the proposed algorithm for optimizing waypoints, and (b) benchmark problems based on typical Reconnaissance scenarios in the real environment to prove the increased effectiveness of the Reconnaissance operation. Moreover, an experiment in the SteelBeast simulation system was also conducted.
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MESAS - Route Optimization for Cooperative Aerial Reconnaissance
Modelling and Simulation for Autonomous Systems, 2018Co-Authors: Petr StodolaAbstract:This paper deals with the optimization of routes generated by the Cooperative Aerial Reconnaissance (CAR) model. This high-level model plans the optimal routes for a fleet of unmanned Aerial systems (UAS) in order to carry out the Reconnaissance operation in the area of interest. The route for each UAS is composed of a number of waypoints to be visited in the correct order. This paper further enhances the routes by applying the smoothing algorithm to individual routes for every Aerial system in the fleet. The first part of the paper presents the fundamental parameters of the smoothing algorithm. Next, the evaluation of the approach is performed via a series of experiments. The proposed modifications have been implemented into the Tactical Decision Support System (TDSS) being developed at University of Defence in Brno to support commanders in their decision making processes.
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route optimization for cooperative Aerial Reconnaissance
international conference on Modelling and simulation, 2017Co-Authors: Petr StodolaAbstract:This paper deals with the optimization of routes generated by the Cooperative Aerial Reconnaissance (CAR) model. This high-level model plans the optimal routes for a fleet of unmanned Aerial systems (UAS) in order to carry out the Reconnaissance operation in the area of interest. The route for each UAS is composed of a number of waypoints to be visited in the correct order. This paper further enhances the routes by applying the smoothing algorithm to individual routes for every Aerial system in the fleet. The first part of the paper presents the fundamental parameters of the smoothing algorithm. Next, the evaluation of the approach is performed via a series of experiments. The proposed modifications have been implemented into the Tactical Decision Support System (TDSS) being developed at University of Defence in Brno to support commanders in their decision making processes.
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Improvement in the model of cooperative Aerial Reconnaissance used in the tactical decision support system
The Journal of Defense Modeling and Simulation: Applications Methodology Technology, 2017Co-Authors: Petr StodolaAbstract:This paper deals with the model of cooperative Aerial Reconnaissance. The goal of this high-level model is to explore the area of interest by a fleet of unmanned Aerial systems optimally, which is (mostly) as fast as possible. The model has been implemented into the tactical decision support system to support commanders of the army of the Czech Republic in their decision making. The current paper does not present the model itself, but it enhances the original model by inserting a new parameter which is called angle delay coefficient. In the first part of the paper, the impact of the new parameter on the task of Aerial Reconnaissance is discussed. A series of experiments were proposed and conducted to verify the influence of the coefficient. The second part of the paper further improves the model by smoothing the routes of individual Aerial systems; a new set of parameters are introduced.
