Automation of Inspection Mission Planning Using 4D BIMs and in Support of Unmanned Aerial Vehicle–Based Data Collection

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

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UNMANNED AERIAL VEHICLE USE FOR WOOD CHIPS PILE VOLUME ESTIMATION

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

10.5194/isprs-archives-xli-b1-953-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 953-956

Author(s):

M. Mokroš ◽

M. Tabačák ◽

M. Lieskovský ◽

M. Fabrika

Keyword(s):

Data Collection ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Applied Research ◽

Rapid Development ◽

Volume Estimation ◽

Wood Chips ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Gnss Data

The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring. <br><br> We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points. <br><br> Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.

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Emerging approaches to support dynamic mission planning: survey and recommendations for future research

The Journal of Defense Modeling and Simulation Applications Methodology Technology ◽

10.1177/1548512919898750 ◽

2020 ◽

pp. 154851291989875

Author(s):

Matthew Henchey ◽

Scott Rosen

Keyword(s):

Unmanned Aerial Vehicle ◽

Department Of Defense ◽

Flight Time ◽

Flight Path ◽

Mission Planning ◽

Future Research ◽

Problem Space ◽

New Information ◽

Algorithmic Support ◽

Aerial Vehicle

In the Department of Defense, unmanned aerial vehicle (UAV) mission planning is typically in the form of a set of pre-defined waypoints and tasks, and results in optimized plans being implemented prior to the beginning of the mission. These include the order of waypoints, assignment of tasks, and assignment of trajectories. One emerging area that has been recently identified in the literature involves frameworks, simulations, and supporting algorithms for dynamic mission planning, which entail re-planning mid-mission based on new information. These frameworks require algorithmic support for flight path and flight time approximations, which can be computationally complex in nature. This article seeks to identify the leading academic algorithms that could support dynamic mission planning and recommendations for future research for how they could be adopted and used in current applications. A survey of emerging UAV mission planning algorithms and academic UAV flight path algorithms is presented, beginning with a taxonomy of the problem space. Next, areas of future research related to current applications are presented.

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