Sandra L. Gomez-Coronel

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About

Sandra L. Gomez-Coronel is a researcher at the forefront of bio-inspired robotics and autonomous navigation. Her work centers on developing vision-based control systems that mimic the human visual system, enabling robots to navigate unknown environments without relying on pre-mapped data. In her most cited work, "Bio-inspired Optical Flow-based Autonomous Obstacle Avoidance Control" (2019, 3 citations), she introduced a novel methodology that uses an image model inspired by human vision to define constraints in optical flow estimation, setting her approach apart from conventional techniques. She further advanced this field with "Optical Flow-Hermite and Fuzzy Q-Learning Based Robotic Navigation Approach" (2021, 2 citations), where she integrated fuzzy Q-learning—a reinforcement learning method—to enable real-time decision-making in dynamic settings. Her research also explores evolutionary learning for vision-based navigation (2020, 2 citations), demonstrating a commitment to adaptive, learning-driven autonomy. Though early in her career, Gomez-Coronel’s work has already shaped how bio-inspired algorithms can be applied to robotic perception and control, offering a promising path toward more resilient, human-like autonomous systems.