Real walking in virtual environments for factory planning and evaluation

Nowadays, buildings or production facilities are designed using specialized design software and building information modeling tools help to evaluate the resulting virtual mock-up. However, with current, primarily desktop based tools it is hard to evaluate human factors of such a design, for instance spatial constraints for workforces. This paper presents a new tool for factory planning and evaluation based on virtual reality that allows designers, planning experts, and workforces to walk naturally and freely within a virtual factory. Therefore, designs can be checked as if they were real before anything is built.ISSN:2212-827

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

We present a novel redirected walking controller based on alignment that allows the user to explore large and complex virtual environments, while minimizing the number of collisions with obstacles in the physical environment. Our alignment-based redirection controller, ARC, steers the user such that their proximity to obstacles in the physical environment matches the proximity to obstacles in the virtual environment as closely as possible. To quantify a controller's performance in complex environments, we introduce a new metric, Complexity Ratio (CR), to measure the relative environment complexity and characterize the difference in navigational complexity between the physical and virtual environments. Through extensive simulation-based experiments, we show that ARC significantly outperforms current state-of-the-art controllers in its ability to steer the user on a collision-free path. We also show through quantitative and qualitative measures of performance that our controller is robust in complex environments with many obstacles. Our method is applicable to arbitrary environments and operates without any user input or parameter tweaking, aside from the layout of the environments. We have implemented our algorithm on the Oculus Quest head-mounted display and evaluated its performance in environments with varying complexity. Our project website is available at https://gamma.umd.edu/arc/

Monte-Carlo Redirected Walking: Gain Selection Through Simulated Walks

We present Monte-Carlo Redirected Walking (MCRDW), a gain selection algorithm for redirected walking. MCRDW applies the Monte-Carlo method to redirected walking by simulating a large number of simple virtual walks, then inversely applying redirection to the virtual paths. Different gain levels and directions are applied, producing differing physical paths. Each physical path is scored and the results used to select the best gain level and direction. We provide a simple example implementation and a simulation-based study for validation. In our study, when compared with the next best technique, MCRDW reduced incidence of boundary collisions by over 50% while reducing total rotation and position gain

Planning Redirection for Dynamic Passive Haptics Using Model Predictive Control

Navigating an immersive, virtual environment (VE) is one of the key challenges when a head-mounted display is used. The most natural way of navigating a virtual environment would be walking. But walking in a large virtual environment is practically impossible unless the user is in an equally large, walkable real environment (RE), i.e. a one-to-one mapping between real and virtual worlds. A promising solution for navigating large virtual spaces in a limited real space, such as a room, is Redirected Walking.Redirected Walking (RDW) uses so-called Redirection Techniques (RETs) to guide the walker away from obstacles in the real world. These techniques modify the mapping between VE and RE depending upon user movements in real-time.Hence, a point in a real environment can be mapped to just about any point in a virtual environment at a particular time. This makes the task of redirecting the user toward an object in the real environment that serves as a proxy for an object in the virtual environment a much more complex problem. This problem ultimately boils down to creating a dynamic map between selected real and virtual world points, i.e. an entire one-to-one map between real and virtual environments is not needed. This dynamic map applies redirections not just to avoid obstacles but also to redirect the user such that whenever the user reaches for an object in the virtual world, he/she senses the proxy object in the real world.Computer Scienc

LoCoMoTe – a framework for classification of natural locomotion in VR by task, technique and modality

Virtual reality (VR) research has provided overviews of locomotion techniques, how they work, their strengths and overall user experience. Considerable research has investigated new methodologies, particularly machine learning to develop redirection algorithms. To best support the development of redirection algorithms through machine learning, we must understand how best to replicate human navigation and behaviour in VR, which can be supported by the accumulation of results produced through live-user experiments. However, it can be difficult to identify, select and compare relevant research without a pre-existing framework in an ever-growing research field. Therefore, this work aimed to facilitate the ongoing structuring and comparison of the VR-based natural walking literature by providing a standardised framework for researchers to utilise. We applied thematic analysis to study methodology descriptions from 140 VR-based papers that contained live-user experiments. From this analysis, we developed the LoCoMoTe framework with three themes: navigational decisions, technique implementation, and modalities. The LoCoMoTe framework provides a standardised approach to structuring and comparing experimental conditions. The framework should be continually updated to categorise and systematise knowledge and aid in identifying research gaps and discussions

Redirected Walking in Infinite Virtual Indoor Environment Using Change-blindness

We present a change-blindness based redirected walking algorithm that allows a user to explore on foot a virtual indoor environment consisting of an infinite number of rooms while at the same time ensuring collision-free walking for the user in real space. This method uses change blindness to scale and translate the room without the user's awareness by moving the wall while the user is not looking. Consequently, the virtual room containing the current user always exists in the valid real space. We measured the detection threshold for whether the user recognizes the movement of the wall outside the field of view. Then, we used the measured detection threshold to determine the amount of changing the dimension of the room by moving that wall. We conducted a live-user experiment to navigate the same virtual environment using the proposed method and other existing methods. As a result, users reported higher usability, presence, and immersion when using the proposed method while showing reduced motion sickness compared to other methods. Hence, our approach can be used to implement applications to allow users to explore an infinitely large virtual indoor environment such as virtual museum and virtual model house while simultaneously walking in a small real space, giving users a more realistic experience.Comment: https://www.youtube.com/watch?v=s-ZKavhXxd