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Steering behavior analysis and implementation to simulate crowd tawaf ritual on Augmented Reality Manasik

机译:用于模拟人群Tawaf仪式的转向行为分析与实现,在Manasik中模拟人群Tawaf Ritual

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Steering behavior is behaviors allowed individual elements to navigate their digital environments in a “life-like” manner with strategies for seeking, fleeing, wandering, arriving, pursuing, evading, path following, obstacle avoiding, etc. This technique is widely used in various fields including the field of robotics, artificial intelligence and artificial life, games and animation. In this research technique of steering behavior used to develop macroscopic simulation crowd of tawaf ritual. Some works are done to create crowded simulation more real by modeling, designing, and implementation analysis and test. This was done in order to simulate more realistic crowd tawaf ritual, steering technique to be applied are flocking, following the path, and path-flocking. Implementation is done by performing coding using C ++ programming language, library Opensteer, and the OpenGL. The steering techniques are next evaluated on several pilgrims' general behaviors, i.e. walking to certain direction, collision avoidance, and obstacle avoidance behavior. In the evaluation, the occurrences of unusual behavior in three steering behavior techniques are recorded and then compared. An evaluation involving user has also been done to get a quick feedback. Based on the evaluation results, it can be concluded that steering behavior of the path flocking is a better model for tawaf crowd simulation than the path following or flocking method alone. That simulation implemented on Augmented Reality for Manasik (ARM).
机译:转向行为是行为允许单个元素在A&#x201c中导航他们的数字环境;生活类似的”旨在寻求,逃离,徘徊,到达,追求,追求,追踪,路径追随,障碍避免等策略的方式。这种技术广泛用于包括机器人领域,人工智能和人工生命,游戏和动画的各种领域。在这种转向行为的研究中,用于开发Tawaf仪式宏观模拟人群的转向行为。有些作品是通过建模,设计和实现分析和测试来创建拥挤的模拟。这样做是为了模拟更现实的人群Tawaf仪式,在路径和路径植物之后植入的转向技术是植入的。实现是通过使用C&#x002b执行编码来完成的;+编程语言,库OpenSteer和OpenGL。接下来在几个朝圣者的一般行为中评估转向技术,即步行到某些方向,碰撞避免和避免避免行为。在评估中,记录三种转向行为技术中的异常行为的发生,然后进行比较。涉及用户的评估也已经完成以获得快速反馈。基于评估结果,可以得出结论,路径植绒的转向行为是Tawaf人群模拟的更好模型,而不是单独的路径或植入方法。这种模拟在Manasik(ARM)的增强现实上实施。

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