In this paper a global localization problem of a robot moving in a known environment is considered. The environment is equipped with a relatively sparse set of passive RFID (Radio Frequency IDentification) tags. The robot can detect the presence of the tags when traveling in their proximity and combines this information with the one given by other sensors (e.g. odometry). The RFID measurements are characterized by a highly non Gaussian noise: for this reason in the literature Particle Filter (PF) methods have often been used to fuse these data with the measurements coming from other sensors. In this paper a different approach is pursued, based on the observation that RFID readings can be considered as noisy quantized measurements of the pose of the robot or as noisy dynamic constraints on the pose itself. This allows to exploit the rich literature on Kalman quantized filtering or Kalman constrained estimation, to realize reliable methods with a satisfactory performance which require a computational time significantly lower with respect to the one needed by a PF. Simulative and experimental results will be reported to illustrate the proposed methods.

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Abstract-In this paper we describe an efficient software architecture for object-tracking, based on a stereoscopic vision system, that has been applied to a mobile robot controlled by a PC. After analyzing the epipolar rectification required to correct the original stereo-images, it is described a new valid and efficient algorithm for ball recognition (indeed circle detection) which is able to work in different lighting conditions and in a manner faster than some modified versions of Circle Hough Transform. Then, we show that stereo vision, besides giving an optimum estimation of the 3D position of the object, is useful to remove lots of the false identifications of the ball, thanks to the advantages of epipolar constraint.Finally, we describe a new strategy for ball following, by a mobile robot, which is able to "look for" the object whenever it comes out of the cameras view, by taking advantage of a "block matching" method similar to that of MPEG Video.

An architecture for robot localization and navigation performing fusion among odometry, laser range data and range from a neural stereoscopic vision system is presented. The estimate robot position is used to safely navigate through the environment. The stereoscopic sub system delivers dense information in the entire field of view. This feature allows a safer navigation of the robot, since, for example, arch-like obstacles may be avoided. Experimental results are presented concerning localization and obstacle detection. The precision attained by the system allows safe navigation. The use of visual data is of great importance in many operative scenarios.

An architecture for robot localization and navigation performing fusion among odometry, laser range data and range from a neural stereoscopic vision system is presented. The estimate robot position is used to safely navigate through the environment. The stereoscopic sub system delivers dense information in the entire field of view. This feature allows a safer navigation of the robot, since, for example, arch-like obstacles may be avoided. Experimental results are presented concerning localization and obstacle detection. The precision attained by the system allows safe navigation. The use of visual data is of great importance in many operative scenarios.

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This paper presents an experimental system aimed at rapid prototyping of feedback control schemes for ventricular assist devices, and artificial ventricles in general. The system comprises a classical mock circulatory system, an actuated bellow-based ventricle chamber, and a software architecture for control schemes implementation and experimental data acquisition, visualization and storing. Several experiments have been carried out, showing good performance of ventricular pressure tracking control schemes.

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