Andrew Bradstreet
Papers
3
Total Citations
94
H-Index
3
About
Andrew Bradstreet is a researcher specializing in spacecraft guidance, control, and robotic manipulation for on-orbit servicing applications. His work sits at the intersection of convex optimization and space robotics, with a particular focus on one of the field's most challenging problems: the autonomous capture of tumbling resident space objects — a capability critical for active debris removal and satellite servicing missions. Bradstreet's most significant contribution is a convex-programming-based guidance algorithm designed to enable a spacecraft equipped with a robotic manipulator to safely intercept and capture a tumbling object in orbit. This work, which has accumulated 68 citations since its 2018 publication, is notable for leveraging convex optimization's deterministic convergence properties to produce reliable, onboard-implementable solutions in real time. His earlier 2017 paper extending convex optimization to broader proximity maneuvering scenarios further established this methodological foundation, earning an additional 18 citations. Particularly compelling is Bradstreet's commitment to experimental validation. His hardware-in-the-loop laboratory demonstrations using spacecraft simulators operating on reduced-gravity test beds bridged the gap between theoretical algorithms and physical implementation, lending credibility to the practical deployment of his guidance approaches. His body of work represents a meaningful advance in making autonomous space robotics a tangible reality.
Research Focus
Key Achievements
Top Papers
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