Papers
2
Total Citations
6
H-Index
2
About
Xiaoni Chi is a robotics researcher whose work centers on modular self-reconfigurable robots and indoor mapping technologies. Her most influential contribution, "Motion error analysis of modular self-reconfigurable robot M-Cubes based screw theory" (2011), addresses a critical challenge in modular robotics: ensuring precise pose accuracy during self-reconfiguration. By applying screw theory, Chi systematically analyzed how assembly errors, cantilever deformation, and joint flexibility affect the positioning of execution modules—a fundamental problem that, if unsolved, prevents adjacent modules from connecting properly. This work has accumulated 4 citations and laid groundwork for error compensation in reconfigurable systems. More recently, Chi has advanced indoor mobile robotics with "A Laser Data Compensation Algorithm Based on Indoor Depth Map Enhancement" (2023), tackling the limitation of 2D LiDAR sensors that can only scan at fixed heights, missing obstacles below that plane. Her compensation algorithm enhances depth map quality, improving robot navigation safety. While her citation counts are modest, Chi’s contributions address foundational mechanical and sensing challenges in robotics, demonstrating sustained engagement with practical problems in modular systems and autonomous navigation.
Research Focus
Key Achievements
Top Papers
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