Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
AI-based Air-to-Surface Mission Planning using Predictive Launch Acceptability Region Approach
Date
2021-06-08
Author
Ozdemir, Mustafa Rasit
Cevher, Levent
Ertekin Bolelli, Şeyda
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
222
views
0
downloads
Cite This
© 2021 IEEE.In this paper, a dynamic air-to-surface mission planning strategy based on artificial intelligence (AI) is proposed for targets of opportunity in order to guide the pilot to follow the shortest and safest trajectory by taking advantage of recent advances in military technologies like predictive launch acceptability region (LAR) approach and High Speed 1760. All of the surface targets of an air-to-surface mission are usually planned and loaded to aircraft before the mission. However, sometimes pilots may be suggested to destroy some unanticipated targets which are unplanned. In that case, pilots can be obliged to deviate from the waypoints of the planned mission in order to accomplish the new unplanned task and this can pose a great danger since there could be many potential threats around theater of war. In proposed method, surface threats and predictive launch acceptability region queries are modeled. Then, probabilistic roadmap algorithm with Dubins distance is applied to produce a waypoint trajectory which makes possible to reach closest goal state. The proposed method is proven by constituting a realistic simulation environment based on ROSplane considering mechanical and environmental factors. In total 25 flight simulations, the maximum observed deviation and the mean deviation from a waypoint are calculated as 11.1 meters and 2.2 meters respectively in a 5000m x 5000m x 300m space. Therefore, the results show that the proposed method can dynamically generate waypoint trajectories by using predictive launch acceptability region queries, which are safe and possible to follow.
Subject Keywords
artificial intelligence
,
launch acceptability region
,
mission planning
,
probabilistic roadmap
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85114553718&origin=inward
https://hdl.handle.net/11511/98898
DOI
https://doi.org/10.1109/icmt52455.2021.9502830
Conference Name
8th International Conference on Military Technologies, ICMT 2021
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Artificial intelligence based dynamic mission planning with probabilistic roadmaps and Voronoi diagrams using predictive launch acceptability region approach
Özdemir, Mustafa Raşit; Ertekin Bolelli, Şeyda; Department of Computer Engineering (2021-9-2)
In this thesis, dynamic air-to-surface mission planning strategies based on probabilistic roadmaps and Voronoi diagrams using predictive launch acceptability region approach are proposed for opportunity targets in order to strengthen decision support capabilities of aircraft. Air-to-surface missions are planned in ground support systems and loaded to aircraft before the mission begins. This means that all the waypoints which should be followed during an air-to-surface mission are planned according to va...
Vehicle routing for aerial surveillance with a homogeneous fleet
Tarakçı, Koray; Karasakal, Esra; Karasakal, Orhan; Department of Industrial Engineering (2021-12-10)
In this study, we develop models and solution approaches for planning the surveillance mission of a homogeneous fleet of Unmanned Aerial Vehicles (UAVs). Predefined areas are to be observed while satisfying a minimum probability of target detection. Areas are assumed to be rectangular and discrete. UAVs with electro-optical sensors take off from a base and fly through predefined routes. The endurance of UAVs is limited by the maximum flight distance. The proposed models minimize the total travel distance of...
An investigation for maturity level and roadmap of unmanned aerial vehicle technologies in Turkey
Türk, Afşar; Çakır, Serhat; Department of Science and Technology Policy Studies (2020-10)
This research aims to investigate problems, needs of the UAV industry, and required actions for the future in Turkey, to determine technological maturity criteria, and to prepare a technology roadmap. Qualitative data collected through interviews with actors from institutions/enterprises operating in the UAV industry is analysed, and inferences about the Turkish UAV industry are made, and suggestions for findings are developed. Twenty statements prepared to determine the technology goals for the Turki...
Deep convolutional neural networks with an application towards geospatial object recognition /
Batı, Emrecan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2014)
The passion of human-being to invent intelligent systems becomes more and more meaningful day by day, as the data captured every second by artificial sensors needs to be examined and classified for many applications. The processing of ever-increasing amount of data by defining information explicitly seems nearly impossible, regarding the variability and the amount of the information, which reveals the need for intelligent systems that are capable of learning. Deep learning is a set of algorithms that attemp...
Autonomous quadrotor flight with vision-based obstacle avoidance in virtual environment
Eresen, Aydin; Imamoglu, Nevrez; Efe, Mehmet Onder (Elsevier BV, 2012-01-01)
In this paper, vision-based autonomous flight with a quadrotor type unmanned aerial vehicle (UAV) is presented. Automatic detection of obstacles and junctions are achieved by the use of optical flow velocities. Variation in the optical flow is used to determine the reference yaw angle. Path to be followed is generated autonomously and the path following process is achieved via a PID controller operating as the low level control scheme. Proposed method is tested in the Google Earth (R) virtual environment fo...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. R. Ozdemir, L. Cevher, and Ş. Ertekin Bolelli, “AI-based Air-to-Surface Mission Planning using Predictive Launch Acceptability Region Approach,” presented at the 8th International Conference on Military Technologies, ICMT 2021, Brno, Çek Cumhuriyeti, 2021, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85114553718&origin=inward.