Max Robotics — Global Service Robot Directory + US Certification

About

Sean McGovern’s research lies at the intersection of robotic manipulation, constrained motion planning, and autonomous learning, with a focus on enabling robots to perform complex industrial tasks on 3D freeform surfaces. His major contributions include developing novel approaches for constrained robotic coverage path planning, where he introduced methods for generating UV grids and applying common coverage patterns—such as raster and spiral—onto freeform surfaces for applications like painting, spray coating, and polishing. His work on “UV Grid Generation on 3D Freeform Surfaces for Constrained Robotic Coverage Path Planning” (2022, 11 citations) and “A General Approach for Constrained Robotic Coverage Path Planning on 3D Freeform Surfaces” (2023, 9 citations) has provided foundational tools for ensuring full surface coverage under task constraints. McGovern has also advanced robot learning for physical object understanding, pioneering reinforcement learning and torque-sensing techniques to estimate the center of mass of unknown objects, as seen in his papers with 5 and 4 citations. His research has practical implications for manufacturing and automation, and his work on efficient feasibility checking for continuous coverage motion (2021) further enhances the reliability of constrained manipulation. With a growing citation record, McGovern is shaping the future of autonomous robotic surface treatment and adaptive manipulation.