Vikram Ravi

Centre for Artificial Intelligence and Robotics

Papers

Total Citations

H-Index

About

Vikram Ravi is a robotics researcher whose work centers on the kinematics and control of hyper-redundant serial robots—snake-like manipulators with far more joints than necessary for a given task. His major contributions lie in developing novel redundancy resolution techniques, most notably through the application of tractrix-based algorithms, which allow these highly flexible robots to navigate cluttered environments with improved efficiency and precision. Ravi’s research has direct implications for search-and-rescue operations in disaster zones and for medical robotics, where dexterity in confined spaces is critical. His most-cited paper, “Trajectory Planning and Obstacle Avoidance for Hyper-Redundant Serial Robots” (2017), has garnered 57 citations, underscoring its influence in the field. Through a combination of simulation and experimental validation, Ravi has advanced the practical deployment of hyper-redundant systems, addressing fundamental challenges in obstacle avoidance and motion planning. His work continues to shape how engineers design and control highly articulated robots for real-world applications.