Yunpeng Lv

Papers

Total Citations

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About

Yunpeng Lv is a rising researcher in robotics and intelligent control, with a focus on advancing motion planning and optimization for robotic arms in complex, unstructured environments. His work tackles critical challenges in autonomous manipulation, particularly improving the efficiency and precision of path planning and inverse kinematics (IK) solutions. Lv’s most cited paper, “MMD-RRT: a path planning strategy for robotic arm with improved RRT algorithm in unstructured environments” (2025, 5 citations), introduces a multi-mode dynamic sampling approach that reduces sampling randomness and redundant path nodes, significantly boosting search efficiency. Complementing this, his study “Improved dung beetle optimization algorithm based inverse kinematics solution for robotic arm” (2025, 2 citations) presents the ECDBO algorithm, which enhances IK solution accuracy through multi-strategy improvements in population initialization and global search. These contributions demonstrate Lv’s ability to blend bio-inspired optimization with practical robotics, addressing real-world deployment hurdles. Though early in his career, his work is already gaining traction for its innovative fusion of algorithmic refinement and application-driven design, marking him as a promising voice in the fields of robotic arm control and autonomous systems.