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About

Supriya Sahu’s research focuses on the structural and motion optimization of industrial robots, particularly six-axis articulated arms used in manufacturing. Her work bridges finite element analysis (FEA) and intelligent path planning to improve robot performance. Her most cited paper, “A Vibration Analysis of a 6 Axis Industrial Robot Using FEA” (2017, 22 citations), uses ANSYS to model and mitigate harmful vibrations, enhancing operational stability. She extends this with “Stress and Modal Analysis of Six-Axis Articulated Robot Using ANSYS” (2018, 7 citations), identifying critical stress points to prevent mechanical failure. In path planning, Sahu applies particle swarm optimization (PSO) and fuzzy logic to solve the common pick-and-place task, as seen in “PSO Based Path Planning” (2019, 5 citations) and “Fuzzy Logic Based Path Planning” (2018, 3 citations), where she prioritizes collision-free, efficient trajectories. Her recent work, “Geometrical significance of trajectory planning through polynomial equation – Customized RPPPRR model robot” (2023, 2 citations), introduces a novel polynomial-based approach for a custom RPPPRR robot, demonstrating her ongoing innovation in trajectory design. With a total of 41 citations across six papers, Sahu’s contributions are valuable for students and engineers seeking to enhance robot reliability and autonomy in industrial automation.