Juan A. Carretero

University of New Brunswick, University of Victoria

Papers

Total Citations

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About

Juan A. Carretero is a leading researcher in robotics and mechanical engineering, with a primary focus on parallel manipulators, motion planning, and computational geometry. His most influential work, "Solving the dynamic equations of a 3-PRS Parallel Manipulator for efficient model-based designs" (2016, 8 citations), has advanced the development of accurate and computationally efficient inverse dynamic models, enabling optimal design and parameter identification for parallel robotic systems. Carretero has also made significant contributions to safe robot motion through his "Length-Optimized Smooth Obstacle Avoidance for Robotic Manipulators" (2011, 5 citations), which introduced a novel algorithm combining polynomial trajectory planning with harmonic functions to generate smooth, collision-free paths. Earlier in his career, he developed a "Genetic Algorithm for calculating minimum distance between convex and concave bodies" (2001, 2 citations), a foundational method for distance determination in robot path planning and physical system simulation. Carretero’s work bridges theoretical modeling with practical robotic applications, and his research on parallel manipulators continues to influence the design of high-precision robotic systems across manufacturing and automation industries.