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使用优化对齐和人工潜力场的重定向控制器的新设计和评估.
IEEE transactions on visualization and computer graphics
|October 3, 2023
概括
新的重定向步行控制器通过最小化用户重置来改善虚拟现实导航. 与现有技术相比,这些基于对齐和集成的方法提供了更流的虚拟机动体验.
科学领域:
- 虚拟现实 虚拟现实 虚拟现实
- 人与计算机的交互
- 机器人技术 机器人技术 机器人技术
背景情况:
- 重定向步行 (RDW) 能够在超越物理空间限制的虚拟环境中实现自然运动.
- 当前的RDW控制器在保持无用户体验和最大限度地减少中断方面面临着挑战.
研究的目的:
- 提出和评估新的优化驱动的基于对齐和人工潜力场 (APF) RDW控制器.
- 开发一个综合控制器,将基于对齐和APF的方法结合起来,以提高性能.
主要方法:
- 开发了基于对齐和APF的控制器,使用基于用户标题和目标向量的目标函数.
- 整合了两个控制器,以利用各自的优势.
- 利用基于模拟的研究来评估控制器性能与最先进的方法对比.
主要成果:
- 与现有方法相比,提出的基于对齐和集成控制器显著减少了重置的次数.
- 这些控制器实现了重新设置之间的距离更均的分布.
- 综合控制器通过结合对齐和APF战略的好处来证明其潜力.
结论:
- 基于优化驱动的对齐和集成的RDW控制器在虚拟现实导航中提供了卓越的性能.
- 这些新的方法通过最小化重置和提供更流的运动来增强用户体验.
- 这些发现表明,开发更具身临其境和自然的虚拟现实体验是一个有希望的方向.
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