Papers
19
Total Citations
212
H-Index
9
About
Manlu Liu is a robotics researcher whose work spans autonomous systems, motion control, path planning, and nuclear safety applications. Best known for pioneering research in robotic radioactive source detection, Liu's 2020 paper on autonomous search of radioactive sources through mobile robots has garnered 42 citations, establishing a foundational framework for deploying robots in hazardous nuclear and biological environments. This thread of safety-critical robotics extends to digital twin-based inspection systems and nuclear power plant maintenance, reflecting a consistent commitment to protecting human workers in dangerous settings. Liu's contributions to manipulator control are equally significant, encompassing deep reinforcement learning approaches for robotic arms and sensitive collision detection methods for human-robot interaction environments. His path planning research—ranging from three-dimensional grid environments using genetic algorithms to soft robot obstacle avoidance via improved particle swarm optimization—demonstrates a breadth of algorithmic expertise developed over nearly two decades. Additional work on spherical robot motion control, fuzzy backstepping for dual-arm cooperation, and multi-robot collaborative decision-making for source search rounds out a diverse portfolio. With over 170 total citations across ten landmark papers, Liu has made enduring contributions to both the theoretical and applied dimensions of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Search of Radioactive Sources through Mobile Robots42 citations · 2020
- 2Inspection Robot Based on Offline Digital Twin Synchronization Architecture21 citations · 2022
- 3Manipulator Control Method Based on Deep Reinforcement Learning20 citations · 2020
- 4Manipulator residual estimation and its application in collision detection20 citations · 2018
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- 9Fuzzy backstepping control for dual-arm cooperative robot grasp11 citations · 2015
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