This work presents a novel control algorithm of redirected walking called steer-to-optimal-target (S2OT) for effective real-time planning in redirected walking. S2OT is a method of redirection estimating the optimal steering target that can avoid the collision on the future path based on the user's virtual and physical paths. We design and train the machine learning model for estimating optimal steering target through reinforcement learning, especially, using the technique called Deep Q-Learning. S2OT significantly reduces the number of resets caused by collisions between user and physical space boundaries compared to well-known algorithms such as steer-to-center (S2C) and Model Predictive Control Redirection (MPCred). The results are consistent for any combinations of room-scale and large-scale physical spaces and virtual maps with or without predefined paths. S2OT also has a fast computation time of 0.763 msec per redirection, which is sufficient for redirected walking in real-time environments.
|Title of host publication||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|Publication status||Published - 2019 Mar|
|Event||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Osaka, Japan|
Duration: 2019 Mar 23 → 2019 Mar 27
|Name||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings|
|Conference||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019|
|Period||19/3/23 → 19/3/27|
Bibliographical noteFunding Information:
This work has supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No. NRF-2017R1A2B4005469), and the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2018-0-01419) supervised by the IITP(Institute for Information communications Technology Promotion.)
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Media Technology