Kuo-Shih Tseng

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Self-localization and tracking a moving object is a key technology for service robot interactive applications. Most tracking algorithms focus on how to correctly estimate the acceleration, velocity, and position of the moving objects based on the prior states and sensor information. What has not been studied so far is tracking the partially observable(More)
Estimation of people tracking may become divergent in the presence of occlusion. Since the interactions between people and environments can be mathematically modeled and probabilistically estimated, stream field based tracking provides the solution where the state of the occluded people is estimated by inferring the interactive force between the virtual(More)
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(More)
For more precise applications, axial motion systems have to be controlled for reducing uncertain perturbations of systems. However, the present control algorithms are difficult to deal with because of the highly nonlinear and unknown properties of uncertainties. Although some control algorithms are used to effectively degrade the uncertainty effects, the(More)
Here we introduce a motion tracking or navigation module for medical simulation systems. Our main contribution is a sensor fusion method for proximity or distance sensors integrated with inertial measurement unit (IMU). Since IMU rotation tracking has been widely studied, we focus on the position or trajectory tracking of the instrument moving freely within(More)
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