Papers
11
Total Citations
415
H-Index
9
About
Phone Thiha Kyaw is a robotics and artificial intelligence researcher whose work sits at the intersection of autonomous systems, path planning, and self-reconfigurable robotics. His research has made significant contributions to coverage path planning (CPP), leveraging deep reinforcement learning (DRL) and Travelling Salesman Problem (TSP) frameworks to enable robots to navigate and cover complex environments more efficiently. His 2020 paper on DRL-based CPP for reconfigurable grid-maps has garnered 89 citations, establishing him as a notable voice in autonomous navigation research. A recurring theme across Kyaw's portfolio is the development of self-reconfigurable robots — systems capable of adapting their physical shape to tackle diverse real-world challenges. His work on the sTetro and hTetran robot families addresses autonomous floor, staircase, and surface cleaning, while his research on ship hull maintenance robots, including the Hornbill system, demonstrates the practical industrial impact of his designs, collectively accumulating over 100 citations across related publications. His 2022 work on energy-efficient path planning for reconfigurable robots further highlights his focus on optimizing both performance and resource consumption. With a growing body of work exceeding 400 cumulative citations, Kyaw's research meaningfully advances the frontier of intelligent, adaptable robotic systems for real-world deployment.
Research Focus
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Top Papers
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- 9sTetro-D: A deep learning based autonomous descending-stair cleaning robot16 citations · 2023
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