Chamira U. S. Edussooriya

Papers

Total Citations

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About

Chamira U. S. Edussooriya’s research bridges the critical gap between human dexterity and autonomous robotic perception, with key contributions in grasping mechanics and advanced radar signal processing. His early work on slip detection in robotic grasping, published in 2008, introduced a novel force-sensor-based strategy to recognize object slippage in human hands—a foundational insight for designing dexterous robotic grippers that ensure firm, adaptive grasps. This paper has garnered 8 citations, establishing a baseline for tactile sensing in robotics. More recently, Edussooriya has advanced autonomous navigation with his 2024 study on graph neural network (GNN) processors for 77 GHz MIMO radar arrays. By addressing the limited range of FMCW MIMO radars through transmit beamforming and beam scanning, his work enhances obstacle detection and environmental mapping for autonomous robotics. Although still accumulating citations (2 to date), this contribution signals a shift toward AI-driven radar processing. Edussooriya’s trajectory—from human hand analysis to cutting-edge GNN-based radar systems—demonstrates a sustained commitment to improving robotic autonomy, making his research valuable for students and engineers exploring tactile feedback and sensor fusion in robotics.