Daniel Ochoa

Escuela Superior Politecnica del Litoral

Papers

Total Citations

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About

Daniel Ochoa is a leading researcher in robotic manipulation, with a particular focus on industrial grasping and 3D vision systems. His work addresses a critical gap in automation: while multi-finger grippers are widely studied, large-object manipulation often requires vacuum-based solutions. Ochoa’s most cited paper, “A 3D vision based approach for optimal grasp of vacuum grippers” (2017, 26 citations), pioneers a method to determine the optimal picking location for two-suction-cup grippers using 3D vision. This contribution directly improves efficiency and reliability in manufacturing and logistics, where handling bulky items is essential. Beyond this, Ochoa’s research spans perception-driven grasping, sensor integration, and robotic system design, with his work cited by engineers and academics advancing industrial automation. His notable achievement lies in bridging computer vision and practical robotics, offering a data-driven solution to a traditionally heuristic problem. For students and researchers, Ochoa’s work exemplifies how targeted, application-oriented research can solve real-world challenges, making him a key figure in the evolution of smart robotic handling systems.