Novel Design and Evaluation of Redirection Controllers Using Optimized Alignment and Artificial Potential Field

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.