Redirected walking (RDW) enables users to walk naturally in a virtual environment (VE) that is larger than the physical environment (PE). However, relying entirely on redirected walking to navigate large VEs can be challenging. Teleportation, on the other hand, is a popular locomotion technique in VR as it enables rapid travel over long distances without inducing VR sickness. It is thus a practical approach to combine RDW and teleportation for VR navigation, using RDW for nearby destinations and teleportation for distant targets. This study introduces Teleportation with Reorientation (TRO), a technique that inlays reorientation into teleportation to enhance the performance of redirected walking. TRO applies an orientation offset immediately after teleportation, which users counteract by physically turning in place, thereby creating an opportunity to realign their heading to a favorable direction in the PE. A simulation-based evaluation and a user study were conducted to compare TRO against regular teleportations, all used in conjunction with RDW. The results demonstrated that TRO significantly improved the performance of redirected walking compared to regular teleportation. Regarding the overall navigation experience, even though TRO resulted in a higher workload and lower usability because it uses a perceptible reorientation strategy, most participants still considered TRO to be acceptable. In addition, participants indicated that their likeability and preference ratings for the TRO variant featuring one-step turning were the same as those for regular teleportation. These findings suggest that TRO with one-step turning can function as a viable alternative to regular teleportation for VR navigation when both RDW and teleportation are available.

Redirected walking allows users to naturally walk in virtual environments that are larger than their physical surroundings by subtly steering them around real-world obstacles. Alignment-based controllers, such as Vis.-Poly. and Alignment-Optimized, have demonstrated potential in minimizing collision occurrences. However, the assessment of shape similarity using polygonal area has weakened the performance of these controllers since area similarity fails to differentiate between regions with same area but different shapes. This paper presents a weighted area method for approximating the area of a walkable region in front of the user. The area of a walkable region can be estimated by accumulating the subdivided unit areas that intersect the region. These unit areas are assigned weights based on the user's location and orientation. Moreover, Artificial potential fields (APF) value at the center of each unit can be easily incorporated into the weight computation. In a simulation-based evaluation, the Alignment-Optimized controller, implemented using the weighted area method, both without and with the APF value-dubbed as Alignment-Optimized-WA-(AD) and Alignment-Optimized-WA-(AD+APF), respectively-is compared against top-notch controllers including Alignment-Optimized, Vis.Poly., ARC, Alignment+APF-Optimized, APF-S2T, and APF-S2G. The results reveal that both Alignment-Optimized-WA-(AD) and Alignment-Optimized-WA-(AD+APF) exceed the performance of these cutting-edge controllers.

Redirected walking (RDW) enables users to walk naturally within a virtual environment that is larger than the physical environment. Recently, several artificial potential field (APF) and alignment-based redirected controllers have been developed and have been demonstrated to significantly outperform conventional controllers. APF Steer-to-Gradient (APF-S2G) and APF Redirected Walking (APF-RDW) utilize the negative gradient and the total force vector, respectively, which are localized to the user's position. These vectors usually point towards the opposite wall when the user is in corridors, resulting in frequent resets within those regions. This paper introduces the APF Steer-to-Target (APF-S2T), a redirected controller that first finds the target sample point with the lowest score in the user's walkable area in both physical and virtual environments. The score of a sample point is determined by the APF value at the point and the distance from the user's position. The direction from the user's position to the target point is then used as the steering direction for setting RDW gains. We conducted a simulation-based evaluation to compare APF-S2T, APF-S2G, APF-RDW, Visibility Polygon-based alignment (Vis.-Poly.) and Alignment-Optimized controllers in terms of the number of resets and the average distance between resets. The results indicated that APF-S2T significantly outperformed the state-of-the-art controllers.

Redirected walking allows users to naturally locomote within virtual environments that are larger than or different in layout from the physically tracked space. In this paper, we proposed novel optimization-driven alignment-based and Artificial Potential Field (APF) redirected walking controllers, as well as an integrated version of the two. The first two controllers employ objective functions of one variable, which is the included angle between the user's heading vector and the target vector originating from the user's physical position. The optimized angle represents the physical cell that is best aligned with the virtual cell or the target vector on which the designated point has the minimum APF value. The derived optimized angle is used to finely set RDW gains. The two objective functions can be optimized simultaneously, leading to an integrated controller that is potentially able to take advantage of the alignment-based controller and APF-based controller. Through extensive simulation-based studies, we found that the proposed alignment-based and integrated controllers significantly outperform the state-of-the-art controllers and the proposed APF based controller in terms of the number of resets. Furthermore, the proposed alignment controller and integrated controller provide a more uniform likelihood distribution across distance between resets, as compared to the other controllers.