The authors have been developing a mobile robot with sensors for various services in the university campus. A prominent feature of university campus is a substantial amount of pedestrians in the outdoor environment. This feature is also typical in the shopping streets where cars are shutout. This paper proposes an application of a stochastic model for the observation and state transition for detecting mobile objects while the localization process. This model can be treated in the framework of nonlinear Kalman filter. In this paper, we implemented the detection algorithm in the offline mode. We demonstrate the experimental detection results, which validate the usefulness of the proposed algorithm.
Two wheeled inverted pendulum robot which has just two driving wheels is attracted as an interesting mobile robot for several applications over the last decade. In this study, we show a framework for the mobile robot constructed by the inverted pendulum mobile robot for a project "KoRo" in our university. It is necessary to be composed by several sensors, namely sensor fusion techniques, such as a laser scanner, an USB camera, a gyro and so on. The framework of the mobile robot composed by RT middleware component with some sensors were mainly investigated.
In this study, a framework of fusion network with several sensors for an inverted pendulum mobile robot is investigated by developing various services as a campus robot in our university. Several devices has been equipped on the robot for sensors as detecting obstacles, walls, human body, boundary of the ground and so on. In order to implement an algorithm for safety to drive, a framework of sensor fusion network realized by RT-middleware components is explained. As one of example, we show an experimental result using the framework on the campus event.
This paper proposes a localization method of mobile robots with omnidirectional camera. An omnidirectional camera captures images of 360• , hence the view is invariant with respect to the direction of the robot's sensor if the image is processed into correlation. The localization algorithm consists of two steps: regression from the correlation image into the position, and applying Kalman filter by incorporating the dynamic model of the mobile robot. The experiment shows the effectiveness of the proposed approach.
This paper shows a basic approach of stabilizing control and tracking method for keeping a sensing level using a pan-tilt unit on an inverted pendulum mobile robot. The mobile robot has just two wheels, which means that this robot is of an inverted pendulum type. Moreover it has several sensors. In this situation, the pitch angle is not always constant, and the data from the sensors are affected by it. Therefore, it is necessary to obtain precisely their posture such as position and angles, where the sensor devices should be controlled for the sensing level. A framework of tracking control by the pan-tilt unit for the inverted pendulum mobile robot is shown, and further some experimental results are demonstrated on the real robot system KoRo.
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