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Báo cáo hóa học: "Research Article Self-Localization and Stream Field Based Partially Observable Moving Object Tracking"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Self-Localization and Stream Field Based Partially Observable Moving Object Tracking | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 416395 12 pages doi 10.1155 2009 416395 Research Article Self-Localization and Stream Field Based Partially Observable Moving Object Tracking Kuo-Shih Tseng1 and Angela Chih-Wei Tang2 intelligent Robotics Technology Division Robotics Control Technology Department Mechanical and System Laboratories Industrial Technology Research Institute Jiansing Road 312 Taiping Taichung 41166 Taiwan 2 Visual Communications Lab Department of Communication Engineering National Central University Jhongli Taoyuan 32054 Taiwan Correspondence should be addressed to Kuo-Shih Tseng seabookg@gmail.com Received 30 July 2008 Revised 8 December 2008 Accepted 12 April 2009 Recommended by Fredrik Gustafsson Self-localization and object tracking are key technologies for human-robot interactions. Most previous tracking algorithms focus on how to correctly estimate the position velocity and acceleration of a moving object based on the prior state and sensor information. What has been rarely studied so far is how a robot can successfully track the partially observable moving object with laser range finders if there is no preanalysis of object trajectories. In this case traditional tracking algorithms may lead to the divergent estimation. Therefore this paper presents a novel laser range finder based partially observable moving object tracking and self-localization algorithm for interactive robot applications. Dissimilar to the previous work we adopt a stream field-based motion model and combine it with the Rao-Blackwellised particle filter RBPF to predict the object goal directly. This algorithm can keep predicting the object position by inferring the interactive force between the object goal and environmental features when the moving object is unobservable. Our experimental results show that the robot with the proposed algorithm can localize itself and track the frequently occluded object. .