Papers
2
Total Citations
45
H-Index
2
About
Ahmed Elazab has made significant contributions to the field of medical robotics, with a primary focus on the inverse kinematics of snake-like robots. His research addresses the critical challenge of controlling highly redundant, flexible manipulators designed for minimally invasive surgery. Elazab’s major contributions include the development of the deeply-learnt damped least-squares (DL-DLS) method, which leverages deep learning to solve the complex inverse kinematics problem for snake-like robots, achieving superior accuracy and efficiency over traditional approaches. This work, published in 2018, has garnered 30 citations, reflecting its impact on the field. Additionally, he proposed a non-iterative geometric approach for inverse kinematics of the redundant lead-module in a radiosurgical snake-like robot, offering a computationally efficient alternative. This method, published in 2017 with 15 citations, provides a practical solution for real-time control. Elazab’s work is foundational for advancing snake-like robotic designs, which hold promise for enabling safer, less invasive access to internal organs, ultimately improving patient outcomes in surgical procedures.
Research Focus
Key Achievements
Top Papers
- 1
- 2