Optimizing Constrained-Environment Redirected Walking Instructions Using Search Techniques

Abstract

A goal of redirected walking (RDW) is to allow large virtual worlds to be explored within small tracking areas. Generalized steering algorithms, such as steer-to-center, simply move the user toward locations that are considered to be collision free in most cases. The algorithm developed here, FORCE, identifies collision-free paths by using a map of the tracking area's shape and obstacles, in addition to a multistep, probabilistic prediction of the user's virtual path through a known virtual environment. In the present implementation, the path predictions describe a user's possible movements through a virtual store with aisles. Based on both the user's physical and virtual location / orientation, a search-based optimization technique identifies the optimal steering instruction given the possible user paths. Path prediction uses the map of the virtual world; consequently, the search may propose steering instructions that put the user close to walls if the user's future actions eventually lead away from the wall. Results from both simulated and real users are presented. FORCE identifies collision-free paths in 55.0 percent of the starting conditions compared to 46.1 percent for generalized methods. When considering only the conditions that result in different outcomes, redirection based on FORCE produces collision-free path 94.5 percent of the time.