Predictive multiuser redirected walking using artificial potential fields

IF 3.2 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Christian Hirt, Noah Isaak, Christian Holz, Andreas M. Kunz
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Abstract

Real walking is considered as the best locomotion metaphor to explore virtual environments in terms of user experience. In addition to being intuitive for the user, walking captures the true feelings of motion since the visual and proprioceptive sensations are harmonized well. The major disadvantage of choosing walking over other locomotion metaphors involves the physical constraints of the available space, which is usually considerably smaller than the virtual environment. To address this issue, redirected walking (RDW) introduces slight mismatches between a user’s visually perceived path and their actual walking pattern, compelling them to subconsciously compensate for the inconsistency by adjusting their walking trajectory. As a result, users are steered to a certain degree, and expansive virtual environments are effectively compressed into smaller physical spaces. Among others, particularly predictive RDW offers immense potential for growth since it unites various algorithmic systems, whereas many approaches from literature depend on drastic restrictions like single-user constraints or architectural limitations to ensure real-time performance. This work presents two novel predictive RDW systems that allow multiple physically colocated users to explore independent and unconstrained virtual environments. The systems rely on two new implementations of prediction systems based on clothoid trajectory generation combined with a cost-based planning concept built on non-harmonic artificial potential fields (APFs), which inherently allow non-convex and dynamic physical environments. Using the APFs, three additional RDW conditions popular in the literature are implemented for comparison purposes. The five RDW concepts are then validated in an extensive user study with 150 participants conducted in 75 pairs. The results indicate that the novel predictive RDW systems outperform the three systems from literature, except for particular sections of the virtual environment with specific architectural traits.
利用人工势场进行预测性多用户重定向行走
就用户体验而言,真实行走被认为是探索虚拟环境的最佳运动隐喻。对用户来说,步行不仅直观,而且还能捕捉到运动的真实感受,因为视觉和本体感觉得到了很好的协调。与其他运动隐喻相比,步行的主要缺点是受到可用空间的限制,因为可用空间通常比虚拟环境小得多。为了解决这个问题,重定向行走(RDW)在用户的视觉感知路径和实际行走模式之间引入了轻微的不匹配,迫使他们下意识地通过调整行走轨迹来弥补这种不一致。因此,用户在一定程度上被引导,广阔的虚拟环境被有效压缩到较小的物理空间中。其中,特别是预测性 RDW 具有巨大的发展潜力,因为它融合了各种算法系统,而文献中的许多方法都依赖于单用户限制或架构限制等严格限制来确保实时性能。本研究提出了两种新型预测性 RDW 系统,允许多个物理上位于同一地点的用户探索独立且无约束的虚拟环境。这两个系统依赖于基于布状轨迹生成的预测系统的两个新实施方案,并结合了基于非谐波人工势场(APF)的成本规划概念。为了便于比较,我们使用人工势场实现了文献中流行的另外三种 RDW 条件。然后,在一项广泛的用户研究中对这五种 RDW 概念进行了验证,该研究有 150 名参与者,分 75 对进行。结果表明,新颖的预测性 RDW 系统优于文献中的三种系统,但具有特定建筑特征的虚拟环境的特定部分除外。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.80
自引率
0.00%
发文量
0
审稿时长
13 weeks
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