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About

Victor Parque is a robotics and autonomous systems researcher whose work spans mobile robot navigation, path planning, and intelligent control systems. His research is distinguished by its integration of advanced computational techniques — including fuzzy logic, deep learning, and evolutionary algorithms — to solve real-world challenges in robotic autonomy and safety. Parque's most influential contribution, "Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic" (2020, 53 citations), demonstrates his pioneering application of Z-number theory to improve robustness and smoothness in robot navigation under uncertainty. His broader body of work on smooth path planning — employing differential evolution, B-spline curves, and aesthetic fairness functionals — reflects a sustained commitment to making autonomous navigation both safe and comfortable for practical deployment. Beyond classical robotics, Parque has expanded into cutting-edge application domains: his deep learning-based agricultural pest detection system (2023, 20 citations) showcases interdisciplinary impact in precision farming, while his e-textile sensor design for continuum robots (2023, 11 citations) highlights contributions to soft robotics hardware. His research on deep reinforcement learning for navigation in human-populated environments further underscores his forward-looking vision. Collectively, his work has garnered over 140 citations, establishing him as a versatile and impactful contributor to modern robotics research.