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About

Chaoyi Sun is a robotics researcher whose work focuses on motion planning and trajectory optimization for autonomous systems operating in complex, real-world environments. Sun’s primary contributions lie in developing computationally efficient algorithms that bridge the gap between theoretical optimization and practical robotic deployment. Their most cited work, "A Successive Linearization in Feasible Set Algorithm for Vehicle Motion Planning in Unstructured and Low-Speed Scenarios" (2021, 34 citations), tackles the fundamental challenge of non-convex collision avoidance and nonlinear vehicle dynamics by introducing a novel successive linearization technique that ensures feasible, real-time solutions. This work is critical for mobile robots navigating cluttered, unpredictable spaces. Sun has also advanced path planning with the "Gridmap-Path Reshaping Algorithm" (2019, 18 citations), which refines initial paths through convex relaxation—a departure from traditional roadmap methods. Most recently, Sun has applied these principles to construction automation, addressing the multi-target coverage trajectory planning problem for ceiling painting robots (2025). By combining rigorous optimization with practical constraints, Sun’s research directly impacts autonomous driving, service robotics, and industrial automation, making their work essential reading for students and engineers developing next-generation mobile systems.