M. Orlandelli
Papers
1
Total Citations
22
H-Index
1
About
M. Orlandelli is a researcher at the forefront of guidance, navigation, and control (GNC) systems for planetary exploration, with a particular focus on autonomous landing technologies. Their most-cited work, a 2020 paper introducing a recurrent deep architecture for quasi-optimal feedback guidance in planetary landing (22 citations), addresses a critical challenge in spaceflight: enabling precise, real-time trajectory adjustments during descent onto large planetary bodies. This contribution is pivotal for future human and robotic missions, as it moves beyond traditional, computationally expensive optimization methods toward efficient, neural-network-based solutions that can operate onboard a spacecraft. Orlandelli’s research directly supports the development of robust landing systems, a key enabler for exploring the solar system’s surfaces. By blending deep learning with classical control theory, they are helping to make autonomous precision landing a practical reality, with implications for missions to the Moon, Mars, and beyond. Their work is a testament to the growing role of artificial intelligence in space exploration, offering a path toward safer and more capable landers.
Research Focus
Key Achievements
Top Papers
- 1