Papers
5
Total Citations
61
H-Index
3
About
Zhitao Liu is a robotics researcher whose work centers on safe, scalable multi-robot navigation and collision avoidance. His primary contributions lie at the intersection of control theory and motion planning, where he has pioneered novel approaches that integrate velocity obstacles with control barrier functions (CBFs). In his most cited work, "Velocity Obstacle for Polytopic Collision Avoidance for Distributed Multi-Robot Systems" (36 citations), Liu addressed the challenge of real-time, distributed collision avoidance for robots with complex polygonal shapes—a problem that had previously limited scalability. He further advanced the field by developing time-varying CBFs for unicycle-modeled robots, enabling simultaneous control of both linear and angular velocities for dynamic obstacle avoidance. His 2025 paper on acceleration-controlled unicycle robots tackles the difficult problem of designing valid safety-critical controllers when control inputs do not appear directly in constraint functions. Liu has also extended his safety frameworks to whole-body collision avoidance and applied deep learning to underwater object detection for robotic picking. His work is notable for bridging theoretical rigor with practical, real-time implementation, making him a rising figure in safe autonomous navigation.
Research Focus
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