Autonomous Vision-Based Navigation and Control for Robotic Space Objects


Next generation robotics and flying objects are beginning to appear more and more within industrial applications and research environments alike. The demand for the derivation of new dynamical models, estimation and control algorithms for space situational awareness and object management has become apparent. This research seeks to enable autonomous real-time detection, tracking, orbit determination, and collision avoidance with respect to many resident space objects. Key measurements are bearing angles obtained by one or multiple cooperative observer satellites through simple passive optical cameras working in the visible or infrared spectrum. As such, this responds to the need to autonomously detect, perform accurate orbit determination and safe collision avoidance for large numbers of uncooperative resident space objects. This capability would enable autonomous navigation in GNSS-denied environments, improved data products and association for space situational awareness, and new classes of distributed space missions.

Brief Biography

Dr. Hesham Shageer is an Assistant Research Professor with the King Abdulaziz City for Science and Technology (KACST). He is also Co-Director and Principal Investigator with the collaborative research Center of Excellence for Aeronautics and Astronautics (CEAA) a joint scientific effort between KACST and Stanford University.  His research experience includes Adaptive Control Theory and Design Methodologies, System Modeling and Numerical Simulations, as well as Optimal Experimental Design. Dr. Shageer received his B.S. (2004), M.S. (2006) and Ph.D. (2013) degrees from the University of Virginia (UVA), all of which with a major in Electrical Engineering and a specialization in Control Systems.  While at UVA, he participated on a novel aircraft control design project funded by NASA, in addition to participating in the Solar Decathlon Design Competition. His research interests span multiple domains including Machine Learning Based Adaptive Control Synthesis, Trajectory Optimization for Flying Robots, Autonomous Aircraft System Design, Green Engineering and Bio-mimicking Design Concepts.

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