About

Thavida Maneewarn is a prominent robotics researcher whose work spans mobile robotics, teleoperation, inspection systems, and multi-robot mechanics. Based at a leading Thai research institution, Maneewarn has made significant contributions to intelligent robot control, particularly through pioneering work on fuzzy Q-learning for mobile robots, where a novel reward-sharing mechanism and exploration strategy substantially improved autonomous learning performance (26 citations). Early in their career, Maneewarn contributed to cutting-edge telerobotics, developing systems for handling delicate protein crystals aboard the International Space Station and advancing haptic feedback methods for telerobotic control (23 and 12 citations, respectively). A recurring theme throughout their research is locomotion in challenging environments — from dynamic modeling of one-wheel robots using Kane's method to snake robots navigating inclined pipes, humanoid walking on slopes, and magnetically-driven in-pipe inspection robots capable of traversing both horizontal and vertical ferromagnetic surfaces (19 citations). Maneewarn has also explored the fundamental mechanics of cooperative multi-robot manipulation using flexible tools. With a body of work totaling well over 150 citations, their research has meaningfully advanced practical robotic systems for industrial inspection, space applications, and autonomous navigation.

Research Focus

Key Achievements

10
H-Index
32
Papers
269
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
A modifled approach to fuzzy Q learning for mobile robots
26 citations · 2005
📈 Most Prolific Year: 2014 (3 Papers)
🤝 Key Collaborators: 43
🏛 Institutions: King Mongkut's University of Technology Thonburi, University of Washington, King Mongkut's University of Technology North Bangkok, Robotics Research (United States)

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago