Kun Su

Shanghai University of Engineering Science, Rensselaer Polytechnic Institute

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About

Kun Su’s research lies at the intersection of robotics, swarm intelligence, and environmental monitoring, with a focus on enabling autonomous systems to navigate and reason in complex, dynamic environments. In his influential work on robot path planning, Su introduced a Random Coding Particle Swarm Optimization algorithm, offering a novel solution to the classic problem of obstacle avoidance and optimal route generation—a foundational contribution that has garnered 15 citations and remains relevant to mobile robotics. Expanding into multi-robot systems, Su tackled the challenging problem of parameter identification in spatial–temporal varying processes, such as realistic diffusion fields. His 2020 study demonstrated how a coordinated team of mobile robots could robustly estimate parameters of nonlinear partial differential equations, bridging theoretical control with practical environmental sensing. This work, with 13 citations, showcases his ability to address real-world uncertainty through decentralized cooperation. Su’s contributions are notable for their dual emphasis on algorithmic innovation and experimental validation, positioning him as a researcher who advances both the theory and application of intelligent robotic systems in uncertain, time-varying environments.