Amal Gunatilake
Papers
10
Total Citations
148
H-Index
5
About
Amal Gunatilake is a researcher whose work sits at the critical intersection of robotics, non-destructive evaluation, and infrastructure asset management. Her primary research focuses on developing advanced sensing and localization technologies for in-pipe robots, addressing the urgent global challenge of aging underground water pipe networks. Gunatilake’s major contributions include pioneering the integration of stereo vision with laser profiling for high-fidelity 3D mapping of internal pipe defects, a method detailed in her most-cited work (77 citations). She has also been instrumental in advancing battery-free UHF-RFID sensor systems, combined with sophisticated algorithms like Gaussian processes and particle filters, to enable reliable robot localization in GPS-denied pipe environments—a persistent bottleneck in the field. Her work on RFID-based Simultaneous Localization and Mapping (SLAM) for in-pipe perception (14 citations) further demonstrates her impact. With a growing body of work accumulating over 140 citations, Gunatilake’s research directly supports proactive maintenance strategies for utilities, promising to reduce costly pipe failures and public disruptions. Her recent exploration of deep reinforcement learning for socially-aware robot navigation signals an expanding scope into human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 3Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception14 citations · 2022
- 4Using UHF-RFID Signals for Robot Localization Inside Pipelines11 citations · 2021
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- 6Using UHF-RFID Signals for Robot Localization Inside Pipelines3 citations · 2021
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- 8Using UHF-RFID Signals for Robot Localization Inside Pipelines2 citations · 2021
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