Alexis E. Guadarrama-Munoz

Papers

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About

Alexis E. Guadarrama-Munoz is a researcher focused on bio-inspired robotics and autonomous navigation, with a particular emphasis on vision-based control systems. His major contribution lies in developing novel methodologies for obstacle avoidance that draw directly from biological vision principles. In his most cited work, "Bio-inspired Optical Flow-based Autonomous Obstacle Avoidance Control" (2019), he proposed a unique approach that models image constraints after the human visual system, setting it apart from conventional optical flow techniques. This work, with 3 citations, represents a foundational step in bridging computational vision with biological perception for real-time robotic applications. Guadarrama-Munoz’s research has implications for safer, more efficient autonomous systems, particularly in environments where traditional sensors may fail. His interdisciplinary approach—combining neuroscience, computer vision, and control theory—has positioned him as an emerging voice in bio-inspired robotics, offering fresh perspectives on how machines can perceive and navigate the world.