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
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Báo cáo hóa học: "Research Article Self-Localization and Stream Field Based Partially Observable Moving Object Tracking"Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2009, Article ID 416395, 12 pagesdoi:10.1155/2009/416395Research ArticleSelf-Localization and Stream Field Based PartiallyObservable Moving Object Tracking Kuo-Shih Tseng1 and Angela Chih-Wei Tang2 1 IntelligentRobotics 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. Compared with the traditional Kalman filter and particle filter-based algorithms, the proposed one significantly improves the tracking accuracy. Copyright © 2009 K.-S. Tseng and A. C.-W. Tang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.1. Introduction on object tracking and robot localization for interactive navigation applications.Navigation in a static environment is essential to mobile In the previous work, most tracking algorithms aim atrobots. The related research topics consist of self-localization, correctly estimating the position, velocity, and accelerationmapping, obstacle avoidance, and path planning [1]. In of moving objects based on the object motion model,a dynamic environment, it becomes interactive navigation sensor model, sensor data at time t and states estimated at time t − 1, [3]. For example, the Kalman filter withincluding leading, following, intercepting, and people avoid-ance [2]. The major concern of following is how to track a constant velocity model and/or a constant accelerationand to follow moving objects without getting lost. In this model can be used to track moving objects with the linearscenario, the robot should be capable of tracking, following, sensor model [4]. However, the object motion models areself-localization, and obstacle avoidance in a previously usually nonlinear in the real world. Moreover, the objectmapped environment. Following and obstacle avoidance are states are usually with non-Gaussian probability distributionthe problems of decision making while object tracking and so that the Kalman filter with one-hypothesis is poor inrobot localization are the problems of perception. A good the accurate prediction of object motion. A more feasibleperception system improves the accuracy of decision making. solution is adopting the particle filter for object tracking.Robots with the ability of object tracking can accomplish With this, the objects with the nonlinear state transitions,complex navigation tasks ...