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About

Jorge A. Sarapura is a robotics researcher specializing in adaptive control systems and visual servoing for autonomous vehicles and manipulators. His work bridges the gap between theoretical control algorithms and practical implementation in multi-agent robotic systems. Sarapura’s most cited paper (11 citations) introduces a decentralized control framework for coordinating a miniature helicopter with a team of ground robots using artificial vision, demonstrating how heterogeneous robots can collaborate effectively without centralized supervision. He has made significant contributions to adaptive visual servoing, developing controllers that compensate for unknown dynamic parameters and vision system uncertainties in robotic manipulators. His 2021 paper on adaptive 3D visual servoing for SCARA robots (6 citations) uniquely addresses simultaneous uncertainties in robot dynamics, depth estimation, and camera parameters. Sarapura’s research consistently tackles real-world challenges by designing controllers that adapt to imperfect knowledge of system dynamics and sensor characteristics. His work on quadrotor trajectory tracking (2 citations) extends these adaptive control principles to unmanned aerial vehicles. Through his publications, Sarapura has advanced the practical deployment of vision-based control in applications ranging from industrial manipulation to aerial-ground robot coordination, making his research valuable for students and engineers working on autonomous systems with uncertain operating conditions.