期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2011
卷号：8
DOI：10.5772/10529
语种：English
出版社：SAGE Publications
摘要：To make robots coexist and share the environments with humans, robots should understand the behaviors or the intentions of humans and further predict their motions. In this paper, an A*-based predictive motion planner is represented for navigation tasks. A generalized pedestrian motion model is proposed and trained by the statistical learning method. To deal with the uncertainty, a localization, tracking and prediction framework is also introduced. The corresponding recursive Bayesian formula represented as DBNs (Dynamic Bayesian Networks) is derived for real time operation. Finally, the simulations and experiments are shown to validate the idea of this paper.
关键词：Navigation; Behavior Learning; Behavior Understanding; Mobile Robots; Anytime Planning
