About

Liang Lu’s research bridges the gap between industrial automation and intelligent robotics, with a focus on motion planning, autonomous exploration, and collision avoidance. His early work on folding cartons using fixtures (72 citations) introduced a flexible, robot-driven approach to packaging, enabling rapid changeovers without fixed automation. More recently, Lu has advanced autonomous navigation for aerial and mobile robots in unstructured environments. His optimal frontier-based exploration planner (33 citations) and semantics-aware receding horizon planner (12 citations) enhance real-time scene understanding and object-centric mapping. He also developed the Dynamic Window with Virtual Goal method (11 citations) for reactive obstacle avoidance using motion prediction, and an MPC-based framework (9 citations) for safe trajectory re-planning near humans. His 2025 work on a coarse-to-fine fabric alignment system integrates visual servoing and admittance control to automate garment manufacturing tasks traditionally reliant on skilled labor. With a career spanning from packaging automation to cutting-edge UAV exploration and human-safe manipulation, Lu’s contributions demonstrate a sustained impact on both practical manufacturing and autonomous systems research.

Research Focus

Key Achievements

7
H-Index
9
Papers
160
Total Citations
18
Avg Citations/Paper
🏆 Most Cited Paper
Folding cartons with fixtures: a motion planning approach
72 citations · 2000
📈 Most Prolific Year: 2022 (2 Papers)
🤝 Key Collaborators: 26
🏛 Institutions: University of Illinois Urbana-Champaign, Centre for Automation and Robotics, Hong Kong Science and Technology Parks Corporation, Zhejiang University of Technology, Italian Institute of Technology, City University of Hong Kong

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago