Intelligent guidance, navigation and control in autonomous and aerospace robotic systems

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Rapid advances in autonomous systems technologies create constant demand for new sensor/data fusion and intelligent control techniques. Efficient methods of information integration from several sources are needed in rapidly growing number of applications. Extensive research in robotics in the last decade demonstrated that integration of measurements and data using advanced fusion algorithms lead to significant improvement in performance of autonomous systems. In particular, guidance, navigation, and control of unmanned aerial and ground vehicles play a key role. It is also important for flexible manipulators. This proposal builds on applicant's earlier work on sensor information fusion methods and its application to navigation and control of dynamic systems. The proposed research is aimed at integration of large amount of data and measurements to allow efficient localization, positioning and mapping. Sensor and data fusion algorithms will be investigated and developed using sensor data association techniques, statistical methods, particle filtering and estimation techniques. The main effort will be devoted to the development of advanced methods based on extended, ensemble and adaptive Kalman filter. These techniques would allow fusion of large amount of data for nonlinear systems with coloured noise, e.g. fusion of images with global positioning systems, inertial navigation systems and mapping.