A General Reactive Algorithm for Redirected Walking Using Artificial Potential Functions
PubDate: August 2019
Teams: University of Minnesota
Writers: Jerald Thomas; Evan Suma Rosenberg
Redirected walking enables users to locomote naturally within a virtual environment that is larger than the available physical space. These systems depend on steering algorithms that continuously redirect users within limited real world boundaries. While a majority of the most recent research has focused on predictive algorithms, it is often necessary to utilize reactive approaches when the user’s path is unconstrained. Unfortunately, previously proposed reactive algorithms assume a completely empty space with convex boundaries and perform poorly in complex real world spaces containing obstacles. To overcome this limitation, we present Push/Pull Reactive (P2R), a novel algorithm that uses an artificial potential function to steer users away from potential collisions. We also introduce three new reset strategies and conducted an experiment to evaluate which one performs best when used with P2R. Simulation results demonstrate that the proposed approach outperforms the previous state-of-the-art reactive algorithm in non-convex spaces with and without interior obstacles.