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Beschreibung:
New approaches in the research fields of ultrasonic sensing, environment mapping and self-localization, as well as fault detection, diagnosis, and recovery for autonomous mobile systems are presented. A concept of high-resolution ultrasonic sensing by a multi-aural sensor configuration is proposed, which incorporates cross echoes between neighbour sensors as well as multiple echoes per sensor. In order to benefit from the increased sensor information, algorithms for adequate sensor data processing and sensor data fusion have been developed. In this context, it is described how local environment models can be created at different robot locations by extracting geometric primitives from laser range finder data and from ultrasonic sensor data. Additionally, the application of an extended Kalman filter to mobile robot self-localization based on previously modeled and newly extracted geometric primitives is explained. Furthermore, it is demonstrated how local environment models can be merged for building a global environment map. As a supplement for monitoring the state of environmental sensors, a fault detection model has been developed, which consists of sub-models for data from laser range finders and ultrasonic sensors.