重定向策略切换:用于动态环境适应的选择性重定向控制器
IEEE transactions on visualization and computer graphics
|March 4, 2024
概括
选择性重定向控制器 (SRC) 在虚拟现实重定向技术之间动态切换,大大减少了用户的重置. 这种智能系统通过适应用户的环境来优化重定向步行 (RDW) 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) 是一种在有限的物理区域内扩展虚拟空间的技术.
- 现有的RDW控制器经常使用单一的,固定的策略,这可能不适合所有用户环境.
- 需要对重定向策略进行动态调整,以增强用户体验和沉浸在RDW中.
研究的目的:
- 引入选择性重定向控制器 (SRC),这是一个优化RDW的新系统.
- 为了实现基于用户环境的多个重定向控制器之间的动态切换.
- 减少重置次数并改善整体RDW体验.
主要方法:
- 开发了选择性重定向控制器 (SRC) 模型.
- 训练了SRC模型,使用强化学习来进行动态控制器切换.
- 通过模拟和在不同环境中的用户研究,对传统控制器进行SRC性能评估.
- 利用热图可视化来分析SRC的环境决策.
主要成果:
- 与传统的RDW控制器相比,SRC显著减少了重置的次数.
- 系统根据用户的直接环境动态选择最有利的重定向控制器.
- 为了优化体验,SRC有效地在S2C,TAPF,ARC和SRL策略之间交替使用.
结论:
- 选择性重定向控制器 (SRC) 通过动态调整重定向策略,为 RDW 提供了一种优越的方法.
- 通过最大限度地减少干扰和最大限度地提高每个重定向技术的好处,SRC提高了用户体验.
- 这种自适应的控制方法有望为更加身临其境和无的虚拟现实体验提供希望.
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