Alexander Crain
Papers
3
Total Citations
32
H-Index
3
About
Alexander Crain is a leading researcher in space robotics, specializing in the autonomous capture and removal of orbital debris. His work centers on optimal trajectory planning, pseudospectral optimization, and the integration of deep learning for real-time spacecraft guidance. Crain’s major contributions include experimentally validating pseudospectral-based methods for free-floating robot trajectory planning—a foundational paper with 20 citations that demonstrated how different optimization solvers can reliably deploy robotic manipulators in space. His thesis on compliant spacecraft capture (8 citations) systematically addresses the first two critical phases of debris removal: manipulator deployment and target capture. Most recently, Crain has pioneered a computationally lightweight guidance, navigation, and control architecture that combines deep learning vision with pseudospectral optimization to enable the capture of uncooperative, spinning targets (2025, 4 citations). This work represents a significant leap toward real-time, autonomous debris mitigation. With a focused publication record and a clear trajectory from simulation to experimental validation, Crain is establishing himself as a key innovator in the practical application of optimal control and AI to space robotics.
Research Focus
Key Achievements
Top Papers
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