Andrea Brandonisio
Papers
4
Total Citations
49
H-Index
4
About
Andrea Brandonisio is an aerospace researcher specializing in autonomous space systems, with a particular focus on applying deep reinforcement learning (DRL) to the challenges of on-orbit servicing, assembly, and manufacturing (OSAM) missions. His work sits at the intersection of artificial intelligence and space engineering, addressing one of the field's most pressing challenges: enabling spacecraft and robotic systems to operate intelligently and independently in complex, unstructured orbital environments. Brandonisio's most significant contributions include pioneering the use of reinforcement learning for autonomous imaging path-planning around uncooperative space objects — work that has garnered 29 citations and established him as a notable voice in spacecraft autonomy research. His investigations into deep reinforcement learning for fly-around guidance and redundant space manipulator control further demonstrate his commitment to developing practical AI-driven solutions for debris removal and satellite servicing operations. Collectively, his publications have accumulated nearly 50 citations, reflecting growing recognition within the astrodynamics and space robotics communities. His research is particularly timely given escalating concerns about space debris and the increasing demand for cost-effective, autonomous orbital operations. For students and researchers exploring intelligent space systems, Brandonisio's body of work offers a compelling roadmap for integrating modern machine learning techniques into next-generation mission design.
Research Focus
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Top Papers
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