I. Becerra, M. Suomalainen, E. Lozano, K. J. Mimnaugh, R. Murrieta-Cid and S. M. LaValle, "Human Perception-Optimized Planning for Comfortable VR-Based Telepresence," in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6489-6496, Oct. 2020, doi: 10.1109/LRA.2020.3015191
Human perception-optimized planning for comfortable VR-based telepresence
|Author:||Becerra, Israel1,2; Suomalainen, Markku3; Lozano, Eliezer1;|
1Centro de Investigación en Matemáticas (CIMAT), 36023 Guanajuato, México
2Consejo Nacional de Ciencia y Tecnología, CONACyT, 03940 Mexico City, México
3Center of Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020091669627
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-09-16
This letter introduces an emerging motion planning problem by considering a human that is immersed into the viewing perspective of a remote robot. The challenge is to make the experience both effective (such as delivering a sense of presence), and comfortable (such as avoiding adverse sickness symptoms, including nausea). We refer this challenging new area as human perception-optimized planning, and propose a general multiobjective optimization framework that can be instantiated in many envisioned scenarios. We then consider a specific VR telepresence task as a case of human perception-optimized planning, in which we simulate a robot that sends 360 video to a remote user to be viewed through a head-mounted display. In this particular task, we plan trajectories that minimize VR sickness (and thereby maximize comfort). An A* type method is used to create a Pareto-optimal collection of piecewise linear trajectories while taking into account criteria that improve comfort. We conducted a study with human subjects touring a virtual museum, in which paths computed by our algorithm are compared against a reference RRT-based trajectory. Generally, users suffered less from VR sickness, and preferred the paths created by the presented algorithm.
IEEE robotics and automation letters
|Pages:||6489 - 6496|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by Business Finland project HUMORcc 6926/31/2018, Academy of Finland project PERCEPT, 322637, US National Science Foundation grants 035345, 1328018, and Secretaría de Innovación, Ciencia Y Educación Superior SICES grant SICES/CONV/250/2019 CIMAT.
|Academy of Finland Grant Number:||
322637 (Academy of Finland Funding decision)
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