Papers
12
Total Citations
268
H-Index
9
About
Andy Annamalai is a leading researcher in intelligent robotics, specializing in adaptive control, human-robot interaction, and neural learning for multi-manipulator and dual-arm systems. His most impactful work, an admittance-based adaptive cooperative control method for multiple manipulators (80 citations), introduces a novel approach to safely transport objects with output constraints, addressing critical challenges in collaborative robotics. Annamalai has made significant contributions to robot-assisted echography, combining perception, control, and cognition to address technician shortages and physician injuries—a field with growing clinical relevance. His work on discrete-time optimal adaptive RBFNN control (37 citations) and improved neural control using integral Barrier Lyapunov functions (34 citations) has advanced the efficiency of online neural network control for robot manipulators, reducing computational complexity. Annamalai’s innovative teleoperation research, including Kalman filter-based sensor fusion for Baxter robots (29 citations), integrates Kinect and MYO armbands for intuitive motion capture, while his visual servoing and skill-transferring control (19 citations) enhances humanoid dual-arm robot dexterity. With over 260 total citations across his top papers, Annamalai’s work on compliant impedance control and teaching interfaces—tested on Baxter and KUKA iiwa robots—demonstrates a consistent focus on safe, adaptive, and human-centric robotic systems, making him a key figure in modern robotics and automation.
Research Focus
Key Achievements
Top Papers
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- 5An enhanced teaching interface for a robot using DMP and GMR22 citations · 2018
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