Papers
3
Total Citations
102
H-Index
2
About
Yiyang Liu is a robotics researcher whose work focuses on solving fundamental challenges in robot motion planning, inverse kinematics, and multi-robot coordination. Liu’s most impactful contribution is the development of a general robot inverse kinematics solution using an improved Particle Swarm Optimization (PSO) algorithm, which addresses the limitations of traditional closed-form and numerical methods for robots that do not satisfy the Pieper criterion. This work, published in 2021, has already garnered 95 citations, underscoring its significance in enabling efficient and singularity-free inverse kinematics for arbitrary robotic structures. Liu also advanced motion planning by proposing a sampling-based algorithm incorporating the Metropolis acceptance criterion, improving upon the widely used RRT* method to reduce computational overhead while maintaining asymptotic optimality. More recently, Liu introduced a branch-and-bound approach for globally optimal 2D multi-robot relative pose estimation, a critical step for collaborative robotic systems. These contributions demonstrate Liu’s ability to blend optimization theory with practical robotics, making algorithms more robust and efficient. Liu’s work is particularly valuable for researchers tackling real-world deployment challenges in industrial and service robotics, where general-purpose solutions are essential.
Research Focus
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