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.

Eye tracking for locomotion prediction in redirected walking

“…In this way, any number of conditions and repetitions can be done using the maximum available tracking space without redirection or interaction from the experimenter, while providing enough narrative to the user to behave naturally. As already discussed by Zmuda et al [31], we can expect different behavior from people in a narrow maze-like environment in comparison to wide, open spaces. Prediction is also potentially easier in the second case because the traveled path will diverge earlier compared to a narrow maze, since humans generally follow short and smooths trajectories to their target.…”

“…Both Zmuda et al [31] and Nescher et al [14] used graph representations of their respective virtual environments. This graph representations are predefined and consist of nodes and edges connecting two nodes.…”

“…To this end, Zmuda et al [31] introduced the idea of using probabilistic planning to redirected walking. Nescher et al [14] continued in this direction and formulated the selection of the optimal technique to be applied as a model predictive control problem.…”

Planning redirection techniques for optimal free walking experience using model predictive control

“…Nitzsche et al [12] and Su et al [19] suggested an approach that dynamically minimizes the introduced curvature, but does not make imperceptibility a primary goal. A new way for planning RETs was proposed by Zmuda et al [25]. Their algorithm (FORCE) employs probabilistic planning and uses a terminal state evaluation function to determine feasible RETs that have to be used.…”

“…Following the idea of probabilistic planning of redirection [25], the paper introduces a planning framework that is based on a rigorous definition of redirection as an optimization problem. This allows applying optimization techniques from control theory.…”