Optimizing constrained-environment redirected walking instructions using search techniques.

Area of Science:

• Human-Computer Interaction
• Virtual Reality
• Robotics

Background:

• Redirected walking (RDW) enables exploration of large virtual environments within limited physical spaces.
• Existing generalized steering algorithms, like steer-to-center, offer basic collision avoidance but lack adaptability.
• The need for more sophisticated algorithms to improve user experience and immersion in RDW systems is evident.

Purpose of the Study:

• To introduce and evaluate the FORCE algorithm for enhanced collision-free path identification in redirected walking.
• To compare the performance of FORCE against generalized steering methods in virtual environments.
• To improve the efficiency and success rate of navigating virtual worlds within small tracking areas.

Main Methods:

• Developed the FORCE algorithm, incorporating a map of the tracking area's shape and obstacles.
• Implemented multistep, probabilistic prediction of the user's virtual path through a known environment (e.g., a virtual store).
• Utilized a search-based optimization technique, considering both physical and virtual user states to determine optimal steering instructions.

Main Results:

• FORCE successfully identified collision-free paths in 55.0% of starting conditions, outperforming generalized methods (46.1%).
• In scenarios with differing outcomes, FORCE-based redirection achieved a 94.5% collision-free path rate.
• Simulated and real user tests validated the effectiveness of the FORCE algorithm.

Conclusions:

• The FORCE algorithm significantly improves collision-free path generation in redirected walking compared to generalized approaches.
• Predictive path modeling and optimization are key to enhancing RDW system performance.
• FORCE offers a more robust solution for immersive virtual reality experiences within confined physical spaces.