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 method for the integration of sensor data from a thermal imaging sensor and a vision system such as a camera to enable autonomous robots to perform probabilistic target tracking. Person tracking is essential to enable robots to interact with people and to track a target person. It is necessary to integrate multiple types of sensor data to improve the recognition performance because that of a vision system alone is not very good. However, the sensor data from an imaging device are captured asynchronously. Moreover, the interval of the acquisition of the sensor data is different. We use a stochastic model based on the asynchronous updating of the model of the tracking target by using measurement data from the camera and the thermal imaging sensor. The tracking model is implemented using RT-Middleware, which is used as a platform for the construction of distributed networked robots. The experimental results indicate the feasibility of the proposed method.

This paper describes a method for sensor data fusion of thermal imaging sensor and a vision system such as a camera for probabilistic target tracking for autonomous robots. Person tracking is a crucial issue for achieving tasks for communication with people and following the target person by the robots. The integration of multiple kinds of sensor data for validation of the recognition result is desirable because of the uncertainty of recognition results based on the vision system. We use a stochastic model based on asynchronous updating the model of the tracking target by measurement data of the camera and the thermal imaging sensor. Experimental results indicate the feasibility of the proposed method.