Ci Song

University of Michigan–Flint, Beijing Institute of Technology

Papers

Total Citations

H-Index

About

Ci Song’s research bridges the critical gap between multi-robot coordination and precision manufacturing, with a focus on autonomous exploration and robotic machining. In his seminal 2005 work, Song introduced a distributed bidding model for multi-robot area exploration under limited communication—a foundational contribution that has garnered 31 citations and influenced swarm robotics and field robotics. This algorithm enabled reliable, decentralized coordination, addressing real-world constraints like bandwidth and signal loss. More recently, Song has pivoted to robotic side milling, tackling the twin challenges of weak stiffness and poor posture accuracy. His 2024 study on posture optimization and accuracy compensation (7 citations) proposes a novel cutting-plane stiffness index based on chord distribution, directly improving machining precision. In 2025, he advanced this line with an online surface roughness prediction system using parallel ensemble learning (5 citations), enabling real-time quality control for aluminum alloy components. Song’s trajectory—from multi-robot exploration to high-precision manufacturing—demonstrates a rare versatility, applying algorithmic thinking to both autonomous systems and industrial robotics. His work is particularly impactful for researchers in manufacturing automation, where his stiffness metrics and predictive models offer practical tools for enhancing robotic machining accuracy.