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Haptically enabled simulation system for firearm shooting training

  • S.I. : Virtual Reality, Augmented Reality and Commerce
  • Published:
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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.

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(Reproduced with permission from Krompiec and Park (2017)) (colour figure online)

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

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Correspondence to Hailing Zhou.

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Wei, L., Zhou, H. & Nahavandi, S. Haptically enabled simulation system for firearm shooting training. Virtual Reality 23, 217–228 (2019).

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