Papers
2
Total Citations
28
H-Index
2
About
Hesham Shageer is a leading researcher at the intersection of space robotics, trajectory optimization, and machine learning. His work focuses on enabling autonomous robotic systems to operate safely and efficiently in challenging environments, particularly for satellite servicing and on-orbit assembly. Shageer’s most impactful contribution is his pioneering work on learning-based warm-starting for sequential convex programming (SCP), a technique that dramatically accelerates the computation of locally optimal trajectories for robotic and aerospace systems. His 2020 paper on this topic has garnered 24 citations, establishing a new paradigm for fast, reliable motion planning. In related work, Shageer has addressed the critical challenge of perception-constrained manipulation for satellite servicing, developing algorithms that allow robots to plan movements while respecting sensor field-of-view limits. His research is directly relevant to upcoming NASA missions like Restore-L, demonstrating the real-world applicability of his methods. By combining rigorous optimization theory with practical machine learning, Shageer is helping to unlock key capabilities for the rapidly growing satellite servicing industry, making autonomous space operations more robust and computationally tractable.
Research Focus
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Top Papers
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