Subscribe Now Subscribe Today
Science Alert
Curve Top
Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 2 | Page No.: 248-254
DOI: 10.3923/itj.2012.248.254
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Flexible Navigation Strategies by Predicting Human Motion Tendency

Zhiwei Liang, Songhao Zhu and Cheng Yanyun

When service robots present in environments coexist with people, human-aware navigation become an important problem to be addressed. For doing so, this study designed a human-aware motion planner by inference of human motion modes in a camera network. Given a grid map of an indoor environment, human motion mode can be defined as a probabilistic form and fused into a probabilistic grid map in order to adjust robot navigation strategies. First, a two-level learning algorithm is employed to learn motion modes of persons based on collections of trajectories which are recorded by a camera network. Subsequently, a chain of Gaussian distributions are applied to describe each motion mode. Based on these modes, human motion prediction can be computed. Experimental results show the effectiveness of fusing human motion tendency to adapt robot navigation behaviors.
PDF Fulltext XML References Citation Report Citation
  •    K-Means Clustering to Improve the Accuracy of Decision Tree Response Classification
  •    Neural Network for Object Tracking
  •    A New Fuzzy Clustering Algorithm on Association Rules for Knowledge Management
  •    Research on Color Image Segmentation Based on RS for Intelligent Vehicle Navigation
  •    Improved Monte Carlo Localization Algorithm in a Hybrid Robot and Camera Network
How to cite this article:

Zhiwei Liang, Songhao Zhu and Cheng Yanyun, 2012. Flexible Navigation Strategies by Predicting Human Motion Tendency. Information Technology Journal, 11: 248-254.

DOI: 10.3923/itj.2012.248.254






Curve Bottom