Lingling Su

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About

Lingling Su is a researcher advancing the frontiers of multi-robot systems, with a focus on noncommunicative control and digital-twin prediction for complex physical interactions. Her work addresses critical challenges in autonomous manipulation, particularly for deformable and metamorphic objects—such as deformable linear objects (DLOs)—where system coupling and external constraints escalate complexity. Su’s most cited paper (2022, 10 citations) introduces a digital-twin framework integrated with terahertz (THz) communication to predict the transportation of metamorphic objects by multi-robot teams, offering a novel approach to handling real-time, high-fidelity simulation in constrained environments. Her earlier contributions include a noncommunicative memory-pushing fuzzy control strategy (2020, 4 citations) for sensorless multi-robot systems, specifically designed for underground coal mining applications using heavy-duty mobile support robots (HMSRs), and a noncommunicative transportation method for physically connected objects (2021, 3 citations). These works demonstrate Su’s expertise in overcoming communication instability and sensor limitations, with potential impacts on industrial automation, hazardous environment operations, and collaborative robotics. Her research is particularly notable for bridging theoretical control with practical, real-world constraints.