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About

Manoranjan Majji is a leading researcher in autonomous systems, robotics, and aerospace guidance, navigation, and control (GN&C), with a particular focus on sensor fusion, state estimation, and human-robot interaction. His major contributions span from foundational work in visual localization—where he advanced extended Kalman filters for stereo vision-based mapping in field robotics (15 citations)—to pioneering feedback control strategies for tensegrity robotic systems, addressing the unique challenges of maintaining stability through positive string tensions (11 citations). Majji has also made significant strides in human-robot teaming, developing autonomous task assignment algorithms that optimize attention allocation among multiple operators (9 citations). More recently, his work has centered on space applications, including velocimeter LIDAR-based relative rate estimation for autonomous rendezvous and docking (4 citations) and terrain relative navigation for entry, descent, and landing (EDL) systems. Notably, he contributed to the development of NASA’s Six Degree-of-Freedom Tendon Actuated Robot (STAR), a cable-driven emulation platform for evaluating EDL GN&C systems. With a career marked by high-impact, application-driven research, Majji continues to shape the future of autonomous navigation in both terrestrial and extraterrestrial environments.