Liangjun Zhang
Stanford University, University of North Carolina at Chapel Hill
Papers
2
Total Citations
38
H-Index
2
About
Liangjun Zhang is a robotics and computational geometry researcher whose work centers on motion planning for complex articulated systems. His research tackles some of the most challenging problems in robot navigation, particularly the efficient computation of configuration spaces and the development of planning algorithms capable of handling high-degree-of-freedom (DOF) models in constrained environments. Zhang's most notable contribution is his retraction-based Rapidly-exploring Random Tree (RRT) planner, which addresses the notoriously difficult "narrow passage" problem in motion planning. By formulating retraction as a constrained optimization problem and performing iterative refinement on the boundary of C-obstacle space, his 2010 work — cited 24 times — significantly improved planner performance for articulated models. His earlier 2006 work on fast C-obstacle query computation laid important theoretical groundwork by moving beyond simple free-space collision checks to more sophisticated spatial queries, accumulating 14 citations. Together, these contributions reflect a research trajectory focused on making robot motion planning faster, more reliable, and applicable to real-world complexity. His work is particularly valuable to students and practitioners working on robotic manipulation, autonomous navigation, and computational motion planning in cluttered or geometrically complex environments.
Research Focus
Key Achievements
Top Papers
- 1Retraction-based RRT planner for articulated models24 citations · 2010
- 2Fast C-obstacle query computation for motion planning14 citations · 2006