Papers
68
Total Citations
1,339
H-Index
17
About
Sarath Kodagoda is a prominent robotics and autonomous systems researcher whose work spans human-robot interaction, autonomous vehicles, environmental sensing, and infrastructure inspection. Based at the University of Technology Sydney, Kodagoda has made significant contributions to the development of intelligent robotic systems capable of understanding and navigating complex real-world environments. His early foundational work on fuzzy logic-based speed and steering control for autonomous guided vehicles (140 citations) helped establish robust frameworks for mobile robot navigation. He has since advanced robotic perception considerably, developing Gaussian mixture-based hidden Markov models for human activity recognition using 3D skeleton features (141 citations), and pioneering deep learning approaches to scene classification that leverage semantic object relationships (103 citations). His innovative monocular depth estimation technique combining partial laser observation with camera data (126 citations) addressed a critical challenge for cost-constrained robotic platforms. Kodagoda has also driven impactful applied research in infrastructure monitoring, developing stereo vision and laser profiling systems for detecting underground pipe defects (77 citations) and information-driven adaptive sampling strategies for mobile robotic sensor networks (68 citations). With a body of work consistently bridging theoretical machine learning and practical robotic deployment, Kodagoda has established himself as a versatile and influential figure in intelligent robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Fuzzy speed and steering control of an AGV140 citations · 2002
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- 8Human Activity Recognition for Domestic Robots65 citations · 2014
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- 10Laser range data based semantic labeling of places35 citations · 2010