A network robot system integrated with various types of robots via ubiquitous networks is a new concept which introduces an interactive robot living together with people. In this paper, we present a tangible network robot system composed of a mobile robot and vision sensors embedded in an environment, and show human behavior recognition methods necessary for providing diverse services desired by the people in good time. Typical human behaviors are described with logical sensors which are defined through data fusion processes spatially and temporally with the physical sensors of the mobile robot and those in the environment. Our system can be utilized to make human-robot communication and interaction friendlier and smarter

The pose (position and orientation) of a polyhedral object can be determined by using a set of simple light-stripe range sensors. Data from these sensors, however, inherently contains errors which result in pose uncertainty. Therefore, in addition to calculating the pose itself, it is often desirable to estimate how certain the pose determination is. This paper presents a method for estimating the uncertainty in determining the pose of an arbitrarily positioned object with three light-stripe range finders. We analyze the perturbation of a least squares fit of sensed 3D segments to object faces, and obtain a relationship between the sensing error and the object pose error. Experiments demonstrate that the method provides the estimate of accuracy in pose determination.>

The pose (position and orientation) of a polyhedral object can be determined with range data obtained from simple light-stripe range finders. However, localization results are sensitive to where those range finders are placed in the workspace, that is, sensor placement. It is advantageous for vision tasks in a factory environment to plan optimal sensing positions off-line all at once rather than online sequentially. This paper presents a method for finding an optimal sensor placement off-line to accurately determine the pose of an object when using three light-stripe range finders. We evaluate a sensor placement on the basis of average performance measures such as an error rate of object recognition, recognition speed and pose uncertainty over the state space of object pose by a Monte Carlo method. An optimal sensor placement which is given a maximal score by a scalar function of the performance measures is selected by another Monte Carlo method. We emphasize that the expected performance of our system under an optimal sensor placement can be characterized completely via simulation.>