Sajjad Manzoor
Mirpur University of Science and Technology, Hanyang University
Papers
12
Total Citations
151
H-Index
7
About
Sajjad Manzoor is a leading roboticist whose research focuses on bio-inspired robotics, neural control systems, and multi-robot coordination. His major contributions lie in developing central pattern generator (CPG) models—neural oscillators that enable snake-like and salamander robots to produce diverse rhythmic gaits, including serpentine, rectilinear, and side-winding motions. His foundational work, "A unified neural oscillator model for various rhythmic locomotions of snake-like robot" (25 citations), and subsequent studies on gait transitions (16 citations) have been instrumental in advancing autonomous locomotion in unstructured environments. Manzoor also pioneered multi-sensor fusion for underwater robot self-localization using PC/BC-DIM neural networks (31 citations), addressing the critical challenge of GPS-denied navigation. His coordinated multi-robot strategy for capturing moving targets (13 citations) demonstrates expertise in swarm robotics and adaptive control. Notable achievements include the kinematic improvement of the DLR wrist mechanism using spatial antiparallelogram linkages (12 citations) and the design of a shape-adaptive multifunctional robotic gripper (6 citations). With over 140 total citations across his most influential papers, Manzoor’s work bridges neural computation and mechanical design, offering practical solutions for search-and-rescue, underwater exploration, and industrial automation.
Research Focus
Key Achievements
Top Papers
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- 9Modular design of snake robot for various motions implementation5 citations · 2016
- 10Central patten generator based locomotion in inchworm robot3 citations · 2014