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Updated: Jun 18, 2026

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走向可行和安全的区域:基于DRL的重定向步行利用空间可行性透
IEEE transactions on visualization and computer graphics
|August 4, 2025
概括
这项研究引入了一个新的空间可行性意识的重定向控制器,使用深度强化学习 (DRL) 来改善虚拟现实中的障碍回避. 这种新的方法显著减少了物理碰撞,以获得更好的用户体验.
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) 在有限的物理空间中增强虚拟探索.
- 目前的RDW控制器缺乏先进的空间特征提取,以有效避免障碍.
研究的目的:
- 开发一种使用深度强化学习 (DRL) 的空间可行性感知重定向控制器.
- 通过利用全面的空间特征和新的可行性指标,提高避障能力.
主要方法:
- 引入空间可行性 (SWE) 来量化位置安全性和可达性.
- 设计了一个联合奖励功能,集成SWE和虚拟物理对齐.
- 实施了一种新的重置策略,引导用户进入开放区域,并最大限度地减少再次碰撞.
主要成果:
- 与模拟中最先进的方法相比,拟议的DRL控制器显著减少了物理碰撞.
- 实时用户实验验证实了卓越的漫游体验,并在实际虚拟现实环境中提高了安全性.
结论:
- 空间可行感知控制器为虚拟现实提供了RDW的显著进步.
- 这种方法通过有效地减轻物理障碍,提高了用户的安全性和沉浸感.
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