Skip to main content

Advertisement

Springer Nature Link
Log in

Design and simulation of autonomous military vehicle control system based on machine vision and ensemble movement approach

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

On the battlefield, early detection of armored vehicles can have a positive effect. Because according to this issue, timely and appropriate reactions can be done. The purpose of this study is to achieve the required algorithm in the vehicle control system by considering the car sensor vision, which is necessary to identify and determine the equipment needed to control the military drone based on car sensor vision. Today, the use of wireless networks, especially inter-vehicle wireless networks, in military applications is inevitable. Therefore, in the first step of this research, a new method has been proposed to control and steer unmanned vehicles based on car vision. In the proposed method, two 180-degree panoramic cameras with horizontal vision are used from the recorded images. The simulation results of the proposed method show increased accuracy and reduced implementation cost compared to using LIDAR and RADAR technologies. In the second step, a new approach is introduced to identify four common classes of armored vehicles (tanks, personnel carriers, firing tanks, and military vehicles) that are more likely to be present on battlefields. For this purpose, the latest image processing methods, which is deep learning, have been used. The results of the simulation of the proposed approach show the high accuracy of the proposed approach in detecting armored vehicles in a short time. In the third step of this research, a new method has been proposed to increase the connection of wireless networks. In the proposed method, queue theory is used and the results of the simulation of the proposed method show the high efficiency of the method. As a result, accurate and fast detection with unique features makes the users of the system superior.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Wadea M, Mostafa A, Agrawal DP, Hamad A (2017) Enhancing VANET connectivity through utilizing autonomous vehicles. In 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, pp 204–211

  2. Nugraha BT, Su SF (2017) Towards self-driving car using convolutional neural network and road lane detector. In 2017 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT). IEEE, pp 65–69

  3. Khurana P, Sharma A, Singh SN, Singh PK (2016). A survey on object recognition and segmentation techniques. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp 3822–3826

  4. Zhuang J (2016) Compressive tracking based on HOG and extended Haar-like feature. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, pp 326–331

  5. Gaya JO, Goncalves LT, Duarte AC, Zanchetta B, Drews P, Botelho SS (2016) Vision-based obstacle avoidance using deep learning. In 2016 XIII Latin American robotics symposium and IV Brazilian robotics symposium (LARS/SBR). IEEE, pp 7–12

  6. Gode CS, Khobragade AS (2016) Object detection using color clue and shape feature. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, pp 464–468

  7. Abd Ghani K, Yosri H (2011) Application of artificial intelligent for armour vehicle detection using digital image processing for aerial application. Int J Adv Sci Eng Inf Technol 1(2):173–177

    Article  Google Scholar 

  8. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 779–788

  9. Xiaozhu X, Cheng H (2017) Object detection of armored vehicles based on deep learning in battlefield environment. In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, pp 1568−1570. https://doi.org/10.1109/ICISCE.2017.327

  10. Lee (2015) Comparing deep neural networks and traditional vision algorithms in mobile robotics. Swarthmore University

  11. Andreotti F, Carr O, Pimentel MA, Mahdi A, De Vos M (2017) Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG. In 2017 computing in cardiology (CinC). IEEE, pp 1–4

  12. Hassan AN, Kaiwartya O, Abdullah AH, Sheet DK, Raw RS (2018) Inter vehicle distance based connectivity aware routing in vehicular adhoc networks. Wirel Pers Commun 98(1):33–54

    Article  Google Scholar 

  13. Füßler H, Torrent-Moreno M, Transier M, Krüger R, Hartenstein H, Effelsberg W (2006) Studying vehicle movements on highways and their impact on ad-hoc connectivity. ACM SIGMOBILE Mob Comput Commun Rev 10(4):26–27

    Article  Google Scholar 

  14. Resta G, Santi P, Simon J (2007) Analysis of multi-hop emergency message propagation in vehicular ad hoc networks. In Proceedings of the 8th ACM international symposium on mobile ad hoc networking and computing. pp 140–149

  15. Changalvala R, Malik H (2019) LiDAR data integrity verification for autonomous vehicle. IEEE Access 7:138018–138031

    Article  Google Scholar 

  16. Aghayari S, Saadatseresht M, Omidalizarandi M, Neumann I (2017) Geometric calibration of full spherical panoramic Ricoh-Theta camera. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 4:237–245

    Article  Google Scholar 

  17. Bhagoji AN, Cullina D, Mittal P (2017) Dimensionality reduction as a defense against evasion attacks on machine learning classifiers. arXiv preprint arXiv:1704.02654, 2: 1

  18. Gandhi M, Gada D, Desai K, Khanapuri J (2020) Autonomous vehicle. In Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST)

  19. Abiodun TF, Taofeek CR (2020) Unending war on boko haram terror in northeast Nigeria and the need for deployment of military robots or autonomous weapons systems to complement military operations. J DOI, 6(6)

  20. Syed JM, Namburi DL (2020) Improving response time of ambulance using machine intelligence. In 2020 International Conference for Emerging Technology (INCET). IEEE, pp 1–4

  21. PyTorch. Last modified: Oct 10, 2020; Available from: https://pytorch.org/.

  22. Nguyen G, Dlugolinsky S, Bobák M, Tran V, Lopez Garcia A, Heredia I, Hluchý L (2019) Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 52(1):77–124

    Article  Google Scholar 

  23. Wen M, Park J, Cho K (2020) A scenario generation pipeline for autonomous vehicle simulators. HCIS 10(1):1–15

    Google Scholar 

  24. tkinter—Python interface to Tcl/Tk. Last modified: Oct 10, 2020; Available from: https://docs.python.org/3/library/tkinter.html

  25. Sridhar B (2020) Applications of machine learning techniques to aviation operations: promises and challenges. In 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT). IEEE, pp 1–12

  26. Budiharto W, Irwansyah E, Suroso JS, Gunawan AAS (2020) Design of object tracking for military robot using PID controller and computer vision. ICIC Express Lett 14(3):289–294

    Google Scholar 

  27. Kam C, Kompella S, Nguyen GD, Wieselthier JE, Ephremides A (2017) Modeling the age of information in emulated ad hoc networks. In MILCOM 2017–2017 IEEE Military Communications Conference (MILCOM). IEEE, pp 436–441

  28. Shreedhar M, Varghese G (1996) Efficient fair queuing using deficit round-robin. IEEE/ACM Trans Netw 4(3):375–385

    Article  Google Scholar 

  29. Bharghavan V, Lu S, Nandagopal T (1999) Fair queuing in wireless networks: issues and approaches. IEEE Pers Commun 6(1):44–53

    Article  Google Scholar 

  30. Halil İbrahim B, Mehmet D, Eyüp Burak C, Yavuz C, Sedat A, Bilgehan A, Merve Sedef G et al. “ısdfs 2015-Asaf VAROL”

  31. Raina G, Manjunath S, Prasad S, Giridhar K (2015) Stability and performance analysis of compound TCP with REM and drop-tail queue management. IEEE/ACM Trans Netw 24(4):1961–1974

    Article  Google Scholar 

Download references

Funding

No funds, grants, or other supports was received.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Malek-Ashtar University of Technology, Tehran, Iran

    Kourosh Dadashtabar Ahmadi, Ali Jabar Rashidi & Ali Massomi Moghri

Authors
  1. Kourosh Dadashtabar Ahmadi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ali Jabar Rashidi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ali Massomi Moghri
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ali Massomi Moghri.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmadi, K.D., Rashidi, A.J. & Moghri, A.M. Design and simulation of autonomous military vehicle control system based on machine vision and ensemble movement approach. J Supercomput 78, 17309–17347 (2022). https://doi.org/10.1007/s11227-022-04565-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04565-6

Keywords