Adaptive Optimization Algorithm for Resetting Techniques in Obstacle-ridden Environments

Song Hai Zhang, Chia Hao Chen, Zheng Fu, Yongliang Yang, Shi Min Hu

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)
60 Downloads (Pure)


Redirected Walking (RDW) algorithms aim to impose several types of gains on users immersed in Virtual Reality and distort their walking paths in the real world, thus enabling them to explore a larger space. Since collision with physical boundaries is inevitable, a reset strategy needs to be provided to allow users to reset when they hit the boundary. However, most reset strategies are based on simple heuristics by choosing a seemingly suitable solution, which may not perform well in practice. In this paper, we propose a novel optimization-based reset algorithm adaptive to different RDW algorithms. Inspired by the approach of finite element analysis, our algorithm splits the boundary of the physical world by a set of endpoints. Each endpoint is assigned a reset vector to represent the optimized reset direction when hitting the boundary. The reset vectors on the edge will be determined by the interpolation between two neighbouring endpoints. We conduct simulation-based experiments for three RDW algorithms with commonly used reset algorithms to compare with. The results demonstrate that the proposed algorithm significantly reduces the number of resets.

Original languageEnglish
Pages (from-to)2080-2092
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number4
Early online date4 Jan 2022
Publication statusPublished - 30 Apr 2023


  • Adaptive optimization
  • Heuristic algorithms
  • Layout
  • Legged locomotion
  • Navigation
  • Obstacle-ridden area
  • Optimization
  • Redirected walking
  • Redirection
  • Resetting
  • Space exploration
  • Space vehicles

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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