Immersive Virtual Environment systems that utilize Head Mounted Displays and a large tracking area have the advantage of being able to use natural walking as a locomotion interface. In such systems, difficulties arise when the virtual world is larger than the tracking area and users approach area boundaries. Redirected walking (RDW) is a technique that distorts the correspondence between physical and virtual world motion to steer users away from boundaries and obstacles, including other co-immersed users. Recently, a RDW algorithm was proposed based on the use of artificial potential fields (APF), in which walls and obstacles repel the user. APF-RDW effectively supports multiple simultaneous users and, unlike other RDW algorithms, can easily account for tracking area dimensions and room shape when generating steering instructions. This work investigates the performance of a refined APF-RDW algorithm in different sized tracking areas and in irregularly shaped rooms, as compared to a Steer-to-Center (STC) algorithm and an un-steered control condition. Data was generated in simulation using logged paths of prior live users, and is presented for both single-user and multi-user scenarios. Results show the ability of APF-RDW to steer effectively in irregular concave shaped tracking areas such as L-shaped rooms or crosses, along with scalable multi-user support, and better performance than STC algorithms in almost all conditions.

Head-mounted displays (HMDs) and large area position tracking systems can enable users to navigate virtual worlds through natural walking. Redirected walking (RDW) imperceptibly steers immersed users away from physical world obstacles allowing them to explore unbounded virtual worlds while walking in limited physical space. In cases of imminent collisions, resetting techniques can reorient them into open space. This work introduces categorically new RDW and resetting algorithms based on the use of artificial potential fields that "push" users away from obstacles and other users. Data from human subject experiments indicate that these methods reduce potential single-user resets by 66% and increase the average distance between resets by 86% compared to previous techniques. A live multi-user study demonstrates the viability of the algorithm with up to 3 concurrent users, and simulation results indicate that the algorithm scales efficiently up to at least 8 users and is effective with larger groups.