ARC: Alignment-based Redirection Controller for Redirected Walking in Complex Environments

Williams, Niall; Bera, Aniket; Manocha, Dinesh

doi:10.1109/tvcg.2021.3067781

Cited by 59 publications

(27 citation statements)

References 34 publications

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“…Lee et al [22] observed that larger PEs lead to fewer collisions and that non-convex PE shapes like cross, T, and L lead to more collisions than the convex PEs. Williams et al [43] introduced the Complexity Ratio (CR) metric to quantify the navigability of a PE, VE pair. CR is defined as the ratio of the average distance to the nearest object in the PE and VE, averaged across many sampled points.…”

Section: Locomotion In Virtual Environmentsmentioning

confidence: 99%

“…Specifically, we tested the six environment pairs examined in Sect. 4.2 and the three pairs used in [43]. In each environment, we simulated the user traveling along 50 different paths with a random start and end configuration in the VE, and a random starting configuration in the PE.…”

Section: Designmentioning

confidence: 99%

“…Redirection controllers are algorithms that are used to compute the correct redirection that is applied to optimally steer the user away from physical objects [24]. The performance of controllers is typically evaluated by measuring the number of times the user is reset after getting too close to a physical object [4,23,38,43,44], the average distance the user is able to walk before initiating a reset [4,35,38,43], or the average strength of redirection during locomotion [4,22,43]. These performance metrics are useful, but they do not provide any information in terms of how similar is the user's proximity to obstacles in the PE and VE.…”

Section: A11 Performance Analysis Of Rdw Controllersmentioning

confidence: 99%

“…In this example, the user is located in an identical 10m × 10m PE, VE pair with no obstacles. We simulated the user walking on the same virtual path while being steered with three different RDW controllers: artificial potential fields (APF) [38], alignment-based redirection controller (ARC) [43], and steer-to-center (S2C) [18]. For the path segment shown in Fig.…”

Section: A11 Performance Analysis Of Rdw Controllersmentioning

confidence: 99%

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ENI: Quantifying Environment Compatibility for Natural Walking in Virtual Reality

Williams¹,

Bera²,

Manocha³

2022

Preprint

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Figure 1: A visualization of our Environment Navigation Incompatibility (ENI) scores for physical environment paired with three different virtual environments with increasing environment area. Our metric is used to accurately quantify whether it is possible to compute a good mapping between the geometric layouts of these environment. We sample points across the virtual environment to represent the user's position (shown as colored circles) and compute the corresponding point in the physical environment based on comparing the local neighborhoods. Overall, our metric helps us to determine which regions or subsets of the VE are more compatible with the PE. Through this visualization, we can see which regions of the VE are more or less compatible with the PE.

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Section: Locomotion In Virtual Environmentsmentioning

confidence: 99%

Section: Designmentioning

confidence: 99%

Section: A11 Performance Analysis Of Rdw Controllersmentioning

confidence: 99%

Section: A11 Performance Analysis Of Rdw Controllersmentioning

confidence: 99%

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ENI: Quantifying Environment Compatibility for Natural Walking in Virtual Reality

Williams¹,

Bera²,

Manocha³

2022

Preprint

Self Cite

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“…To improve predictive controller accuracy, Zank and Kunz [18] conducted a study in which they predicted the user's route by generating a skeleton graph from the navigation mesh. In addition, a method called alignment-based redirection controller (ARC) guided the user so that their proximity to an obstacle in the physical environment matched their proximity to an obstacle in the virtual environment as much as possible, allowing the user to explore the vast and complex virtual environment while minimizing collisions with obstacles in the physical environment [21]. More recently, machine learning technologies have been applied to redirection [22], [23].…”

Section: B Redirection Controllermentioning

confidence: 99%

Redirection Controller Using Reinforcement Learning

Chang

Matsumoto²,

Narumi

et al. 2021

IEEE Access

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There is a growing demand for redirected walking (RDW) techniques and their application to various virtual and real environments. To apply appropriate RDW methods and manipulation according to the real and virtual environments, the RDW controllers are predominantly used. There are three types of RDW controllers: direct scripted controller, generalized controller, and predictive controller. The scripted controller type pre-scripts the mapping between the real and virtual environments. The generalized controller type employs the RDW method and manipulation quantities according to a certain procedure depending on the user's position in relation to the real space. This approach has the potential to be reused in any environment because it does not require pre-scripting of user movements; however, it is not fully optimized. The predictive controller type predicts the user's future path using the user's behavior and manages RDW techniques. This approach is highly anticipated to be very effective and versatile; however, it has not been sufficiently developed. This paper proposes a novel RDW controller using reinforcement learning (RL) with advanced plannability/versatility. Our simulation experiments indicate that the proposed method can reduce the number of reset manipulations, which is one of the indicators of the effectiveness of the RDW controller, compared to the generalized controller under real environments with many obstacles. Meanwhile, the experimental results also showed that the gain output by the proposed method oscillates. The results of a user study conducted showed that the proposed RDW controller can reduce the number of resets compared to the conventional generalized controller. Furthermore, no adverse effects such as cybersickness associated with the oscillation of the output gain were evinced. The simulation and user studies demonstrate that the proposed RDW controller with RL outperforms the existing generalized controllers and can be applied to users as it only causes cybersickness comparable to that in the case of conventional generalized controllers.INDEX TERMS Heuristic controller, redirected walking, reinforcement learning, virtual reality.

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Virtual/Augmented/Mixed Reality Technologies for Enabling Metaverse

Yuan Zhu,

Lin

2023

Metaverse Communication and Computing Networks

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ARC: Alignment-based Redirection Controller for Redirected Walking in Complex Environments

Cited by 59 publications

References 34 publications

ENI: Quantifying Environment Compatibility for Natural Walking in Virtual Reality

ENI: Quantifying Environment Compatibility for Natural Walking in Virtual Reality

Redirection Controller Using Reinforcement Learning

Virtual/Augmented/Mixed Reality Technologies for Enabling Metaverse

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