Cited by 7Reinforcement Learning in RoboticsAerospace Engineering and Control SystemsUAV Applications and Optimization

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AI simulations and programming environments for drones: an overview

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Research works printed but not linked: 27

  1. K.P. White, R.G. Ingalls, 2009. Introduction to simulation, in: M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin, R.G. Ingalls (Eds.), Proceedings of the 2009 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, pp. 12–23.. · DOI
  2. C. Kamali, S. Jain, Hardware in the loop simulation for a mini UAV, in: 4th IFAC Conference on Advances in Control and Optimization of Dynamical Systems ACODS 49 (1) (2016) 700–705, doi:10.1016/j.ifacol.2016.03.138. · DOI
  3. Al-Mousa, 2019, UTSim: a framework and simulator for UAV air traffic integration, control, and communication, IJARS, 1
  4. Y.K. Ever, B. Sekeroglu, K. Dimililer, Classification analysis of intrusion detection on NSL-KDD using machine learning algorithms, in: In International Conference on Mobile Web and Intelligent Information Systems, Cham, Springer, 2019, pp. 111–122, doi:10.1007/978-3-030-27192-3_9. · DOI
  5. Bermúdez, 2019, Drone challenge: a platform for promoting programming and robotics skills in K-12 education, IJARS, 16, 1
  6. K. Patel, J. Barve, Modeling, simulation and control study for the quad-copter UAV, in: 9th International Conference on Industrial and Information Systems, IEEE, Gwalior, India, 2014, doi:10.1109/ICIINFS.2014.7036590. · DOI
  7. A. French, M. Mozaffari, A. Eldosouky, W. Saad, Environment-aware deployment of wireless drones base stations with Google Earth simulator, in: Workshop on UNmanned Aerial Vehicle Applications in the Smart City: from Guidance Technology to Enhanced System Interaction, IEEE, 2019, pp. 868–873. · DOI
  8. Al-Turjman, 2019, Enhanced deployment strategy for the 5G Drone-BS using artificial intelligence, IEEE Access, 7, 75999, 10.1109/ACCESS.2019.2921729 · DOI
  9. M.A. Ma'sum, M.K. Arrofi, G. Jati, F. Arifin, M.N. Kurniawan, P. Mursanto, W. Jatmiko, Simulation of intelligent Unmanned Aerial Vehicle (UAV) for military surveillance, in: International Conference on Advanced Computer Science and Information Systems, 2013, doi:10.1109/ICACSIS.2013.6761569. · DOI
  10. Mairaj, 2019, Application specific drone simulators: recent advances and challenges, Simul. Model. Pract. Th, 94, 100, 10.1016/j.simpat.2019.01.004 · DOI
  11. A.I. Hentati, L. Krichen, M. Fourati, L.C. Fourati, Simulation tools, environments and frameworks for UAV systems performance analysis, in: 14th International Wireless Communications & Mobile Computing Conference, IEEE, 2018, pp. 1495–1500, doi:10.1109/IWCMC.2018.8450505. · DOI
  12. Al-Turjman, 2017, Seamless key agreement framework for mobile-sink in IoT based cloud-centric secured public safety sensor networks, IEEE Access, 5, 24617, 10.1109/ACCESS.2017.2766090 · DOI
  13. Tan, 2019, Research on optimal landing trajectory planning method between an UAV and a moving vessel, Appl. Sci, 9, 10.3390/app9183708 · DOI
  14. H. Xu, X. Xu, Y. Li, X. Zhu, L. Jia, D. Shi, Trajectory planning of Unmanned Aerial Vehicle based on A* algorithm, in: The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent, Hong Kong, IEEE, China, 2014, doi:10.1109/CYBER. 2014.6917508. · DOI
  15. Iranmanesh, 2019, A novel data forwarding strategy for a drone delay tolerant network with range extension, Electronics, 8, 659, 10.3390/electronics8060659 · DOI
  16. K. Dimililer, Y.E. Kirsal, S.M. Mustafa, Vehicle detection and tracking using machine learning techniques, in: R.A. Aliev, et al. (Eds.), ICSCCW2019, in: 10th International Conference on Theory and Applications of Soft Computing, Computing with Words and Perceptions, AISC 1095, Springer Nature Switzerland AG2020, 2019, pp. 1–9.
  17. Choi, 2019, Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art, Adv. Robot, 10.1080/01691864.2019.1586760 · DOI · Paper page
  18. Dilek, 2015, Applications of artificial intelligence techniques to combating cyber crimes: a review, Int. J. Artif. Intel. Appl, 6, 21
  19. L. Huang, H. Qu, M. Fu, W. Deng, Reinforcement learning for mobile robot obstacle avoidance under dynamic environments, in: Pacific Rim International Conference on Artificial Intelligence, Cham, Springer, 2018, pp. 441–453, doi:10.1007/978-3-319-97304-3_34. · DOI
  20. I. Tuomi, The impact of artificial intelligence on learning, Teaching and Education (2018), doi:10.2760/12297. · DOI
  21. Klaine, 2018, Distributed drone base station positioning for emergency cellular networks using reinforcement learning, Cognit. Comput, 10, 790, 10.1007/s12559-018-9559-8 · DOI
  22. Al-Turjman, 2020, Optimized unmanned aerial vehicles deployment for static and mobile targets monitoring, Comp. Commun. J., 149, 27, 10.1016/j.comcom.2019.10.001 · DOI
  23. M. Imran, A.M. Said, H. Hasbullah, A survey of simulators, emulators and testbeds for wireless sensor networks, in: 2010 International Symposium on Information Technology, IEEE, Kuala Lumpur, Malaysia, 2010, doi:10.1109/ITSIM.2010.5561571. · DOI
  24. H. Neema, B. Potteiger, X. Kousoukos, G. Karsai, P. Volyesi, S. Janos, Integrated simulation testbed for security and resilience of CPS, in: The 33rd ACM Symposium on Applied Computing - Cyber-Physical Systems Track, Pau, France, 2018. doi:10.1145/3167132.3167173. · DOI
  25. Sharma, 2019, Advances in drone communications, state-of-the-art and architectures, Drones, 3
  26. L. Gupta, R. Jain, G. Vaszkun, , Survey of important issues in UAV communication networks, IEEE Commun. Surv. Tutor. (99) (2015).
  27. F. Al-Turjman, M. Abujubbeh, A. Malekoo, L. Mostarda, UAVs assessment in software-defined IoT networks: an overview, Comp. Commun. J. 150 (15) (2020) 519–536. · DOI

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Metadata

Authors
Olawale, O. P., Dimililer, K., & Al-Turjman, F.
Affiliations
Near East University (CY)
Venue
Drones in Smart-Cities
Publisher
Elsevier
Published
2020
Pages
93-106
OpenAlex
W3033878685
Cited by
7
References
0
Citations by year
2025: 2 · 2024: 1 · 2023: 2 · 2022: 1 · 2020: 1
FWCI
1.60
Type
book chapter
Language
en
Open access
closed
Locations
Elsevier eBooks (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
  • Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
Keywords
drone simulation, unmanned aerial vehicles, quadrotor, AI-based simulation, programming environment, programming languages, autonomous drones

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CoChat. (2026). Citation check: AI simulations and programming environments for drones: an overview. https://app.cochat.ai/papers/10.1016/b978-0-12-819972-5.00006-9
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Olawale, O. P., Dimililer, K., & Al-Turjman, F. (2020). AI simulations and programming environments for drones: an overview. Drones in Smart-Cities. https://doi.org/10.1016/b978-0-12-819972-5.00006-9