About

Dikai Liu is a prominent robotics and autonomous systems researcher whose work spans robot autonomy, human-robot interaction, path planning, and assistive technologies. Based at the University of Technology Sydney, Liu has made significant contributions to the development of intelligent robotic systems for real-world applications, including bridge maintenance, rehabilitation, and mobility assistance. His most-cited work, published in the *Journal of Intelligent & Robotic Systems* (2013, 357 citations), reflects his broad influence in robotics research. Liu's contributions extend across diverse domains: his predator-prey-based coverage path planning algorithm (PPCPP, 2019) advances adaptive robot navigation in dynamic environments, while his musculoskeletal modeling approach to estimating physical assistance needs (2013) pushes boundaries in robotic rehabilitation. His early work on evolutionary computing for robot localization (2006) and multi-stage shared control for mobility assistants (2005) demonstrated his long-standing commitment to intelligent, human-centered robotics. Liu also tackled practical challenges such as singularity-robust manipulator control during physical human-robot collaboration and haptic teleoperation, making robotic systems safer and more effective. His research on image contrast enhancement using particle swarm optimization further illustrates his versatility. With hundreds of citations accumulated across fields, Liu's career reflects a sustained impact on both theoretical robotics and applied autonomous systems.

Research Focus

Key Achievements

23
H-Index
133
Papers
2,195
Total Citations
17
Avg Citations/Paper
🏆 Most Cited Paper
Journal of Intelligent & Robotic Systems
357 citations · 2013
📈 Most Prolific Year: 2013 (10 Papers)
🤝 Key Collaborators: 134
🏛 Institutions: University of Technology Sydney, National University of Singapore, Nvidia (United Kingdom), The University of Sydney, Australian Centre for Robotic Vision

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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