Virtual Reality

, Volume 23, Issue 3, pp 217–228 | Cite as

Haptically enabled simulation system for firearm shooting training

  • Lei Wei
  • Hailing ZhouEmail author
  • Saeid Nahavandi
S.I. : Virtual Reality, Augmented Reality and Commerce


Firearm shooting training is of importance in military and law enforcement training tasks. Traditional training usually uses actual firearms or modified bullets that are dangerous and expensive and difficult to evaluate the performance. Firearm training simulation systems provide risk-free alternatives. However, most existing simulation is visual-only, which lacks the immersion on the force feedback. In this paper, we proposed a new firearm training simulation system, which can provide more realistic training by incorporating physic effects on recoil and trigger pull weight. Dynamic, immersive, and repeatable training experiences while imposes no danger to trainees are provided in our system. The system consists of haptics, physics engine, and motion capture. These three components are carefully combined by developing the corresponding techniques of haptic force rendering, visuo-haptic integration, and synchronisation, physics-based dynamic simulation and motion analysis. Compared with existing systems, our training system has more complete functionalities that include visual firearm shooting, force generation, shooting reactions, result analysis and evaluation. Moreover, it is adaptable to off-the-shelf hardware and software packages and thus it can provide flexibility to system scalability and budget. To evaluate the proposed system, two demonstrations are conducted for users where the systems accuracy, immersion and usability are analysed. The results show the effectiveness of our physics-based shooting model and the proposed system on simulating different shooting scenarios.


Virtual training simulation Haptics Physics engine Motion capture 



Funding was provided by Defence Science Institute (Grant No. 50000).


  1. Ariour H, Nehaoua I, Hima S, Seguy N, Espie S (2010) Mechatronics, design, and modelling of a motorcycle riding simulator. IEEE/ASME Trans Mechatron 15:805–818CrossRefGoogle Scholar
  2. Broeren J, Rydmark M, Sunnerhagen K (2004) Virtual reality and haptics as a training device for movement rehabilitation after stroke: a single-case study. Arch Phys Med Rehabil 85(8):1247–1250CrossRefGoogle Scholar
  3. Buttolo P, Hannaford B (1995) Pen-based force display for precision manipulation in virtual environments. In: Hannaford B (ed) Virtual reality annual international symposium, pp 217–224Google Scholar
  4. Conti F, Barbagli F, Morris D, Sewell C (2005) CHAI 3D: an open-source library for the rapid development of haptic scenes. IEEE World Haptics, PisaGoogle Scholar
  5. Dimension F (2004) DELTA haptic device: 6-DOF force feedback interface. Force Dimens Lausanne 33(3):2006–187Google Scholar
  6. Faroque S, Horan B, Adam H, Pangestu M, Joordens M (2016) Haptic technology for micro-robotic cell injection training systems—a review. Intell Autom Soft Comput 22(3):509–523CrossRefGoogle Scholar
  7. Junior A, Gomes G, Junior N, Santos A, Vidal C, Cavalcante-Neto J, Gattass M (2012) System model for shooting training based on interactive video, three-dimensional computer graphics and laser ray capture. In: 14th symposium on virtual and augmented reality, Rio Janiero, pp 254–260Google Scholar
  8. Kadlecek P (2011) Overview of current developments in haptic APIs. In: Proceedings of CESCGGoogle Scholar
  9. Krompiec P, Park K (2017) Enhanced player interaction using motion controllers for VR FPS. In: 2017 IEEE international conference on consumer electronics (ICCE), Las Vegas, NV, pp 19–20Google Scholar
  10. Li S (2009) The design and implementation of shooting system simulation platform for police college. In: 2009 international conference on scalable computing and communications; eighth international conference on embedded computing, Dalian, pp 566–570Google Scholar
  11. Liu G, Lu K (2011) Networked tank gunnery skill training based on haptic interaction. In: Proceedings of international conference on biomedical engineering and informatics, vol 4, pp 2220–2224Google Scholar
  12. Luciano C, Banerjee P, DeFanti T (2009) Haptics-based virtual reality periodontal training simulator. Virtual Real 13(2):69–85CrossRefGoogle Scholar
  13. Marin F, Dominio F, Zanuttigh P (2014) Hand gesture recognition with leap motion and kinect devices. In: 2014 IEEE international conference on image processing (ICIP). IEEEGoogle Scholar
  14. Martin S, Hillier N (2009) Characterisation of the novint falcon haptic device for application as a robot manipulator. In: Australasian Conference on Robotics and Automation (ACRA)Google Scholar
  15. Raisamo J, Raisamo R, Kosonen K (2006) Distinguishing vibrotactile effects with tactile mouse and trackball. In: McEwan T, Gulliksen J, Benyon D (eds) People and computers XIX the bigger picture. Springer, London, pp 337–348CrossRefGoogle Scholar
  16. Rodrigues T, Silva S, Duarte P (2017) The value of textual haptic information in online clothing shopping. J Fash Mark Manag 21(1):88–102CrossRefGoogle Scholar
  17. Ruspini D, Kolarov K, Khatib O (1997) The haptic display of complex graphical environments. In: Proceedings of the 24th annual conference on computer graphics and interactive techniques. ACM Press, pp 345–352Google Scholar
  18. Salisbury J, Srinivasan M (1997) Phantom-based haptic interaction with virtual objects. IEEE Comput Graph Appl 17(5):6–10CrossRefGoogle Scholar
  19. Sewell C, Blevins NH, Peddamatham S, Tan HZ, Morris D, Salisbury K (2007) The effect of virtual haptic training on real surgical drilling proficiency. In: Second joint EuroHaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems (WHC 2007), pp. 22–24Google Scholar
  20. Soetedjo A, Ashari M, Mahmudi A, Nakhoda Y (2014) Implementation of sensor on the gun system using embedded camera for shooting training. In: 2014 2nd international conference on technology, informatics, management, engineering & environment, Bandung, pp 69–74Google Scholar
  21. Sourin A, Wei L (2009) Visual immersive haptic mathematics. Virtual Real 13(4):221–234CrossRefGoogle Scholar
  22. Tong H, Wang J, Duo Y (2010) Combat effectiveness evaluation of firearms system based on MMESE. In: 2010 international conference on information, networking and automation (ICINA), Kunming, pp 342–345Google Scholar
  23. Wei L, Sourin A, Sourina O (2008) Function-based visualization and haptic rendering in shared virtual spaces. Vis Comput 24(10):871–880CrossRefGoogle Scholar
  24. Weichert F, Bachmann D, Rudak B, Fisseler D (2013) Analysis of the accuracy and robustness of the leap motion controller. Sensors 13(5):6380–6393CrossRefGoogle Scholar
  25. Wei L, Huynh L, Zhou H, Nahavandi S (2015) Immersive visuo-haptic rendering in optometry training simulation. In: Proceedings of IEEE international conference on systems, man, and cybernetics (SMC), pp 436–439Google Scholar
  26. Wei L, Zhou H, Soe A, Nahavandi S (2013) Integrating kinect and haptics for interactive STEM education in local and distributed environments. In: Proceedings of IEEE/ASME international conference on advanced intelligent mechatronics, pp 1058–1065Google Scholar
  27. Xia P, Lopes A, Restivo M, Yao Y (2012) A new type haptics-based virtual environment system for assembly training of complex products. Int J Adv Manuf Technol 58(1–4):379–396CrossRefGoogle Scholar
  28. Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimed 19(2):4–10CrossRefGoogle Scholar
  29. Zilles C, Salisbury J (1995) A constraint-based god-object method for haptic display. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems 95. Human robot interaction and cooperative robots, vol 3, pp 146–151Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Intelligent Systems Research and Innovation (IISRI)Deakin UniversityWaurn PondsAustralia

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