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About

Yong Zeng is a robotics and automation researcher whose work centers on spray painting robot systems, with particular focus on trajectory planning, path optimization, and coating quality modeling. Over more than a decade of sustained research, Zeng has made significant contributions to solving fundamental challenges in automated industrial spraying, including paint waste reduction, coating thickness uniformity, and spray efficiency across complex geometries. His most impactful contributions include developing boundary-constrained trajectory planning methods for irregular surfaces, a many-times spray painting optimization framework relevant to automotive manufacturing, and a Gaussian sum model for predicting coating growth rates under varied dip-angle conditions — each garnering approximately 15–16 citations. Earlier work addressed the demanding problem of trajectory optimization on curved surfaces such as cones and spheres, demonstrating a consistent progression from foundational geometric modeling toward more sophisticated, application-ready solutions. Zeng's research is particularly valuable to the automotive and industrial manufacturing sectors, where robotic spray painting demands both precision and material efficiency. His body of work collectively advances the field by bridging theoretical spray deposition modeling with practical robot path generation algorithms, offering researchers and engineers robust tools for deploying more intelligent, waste-reducing automated painting systems.