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About

Josephine N. Bautista is a researcher at the intersection of control theory, robotics, and STEM education, with a focus on making complex engineering concepts accessible through simulation and bio-inspired optimization. Her most cited work, "Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm" (2023, 10 citations), demonstrates her innovative approach to virtual laboratories, enhancing student interaction with control systems through mobile robot simulations. She further explores motion control in "Motion control for micro-robots playing soccer games" (2002, 4 citations), integrating visual perception, path-planning, and behavior coordination. Her recent contributions include "Sub-optimal Perceptron Controller Using WOA Algorithm for Farming Watering Mobile Robot" (2024, 3 citations), where she applies the Whale Optimization Algorithm to optimize artificial neural networks for agricultural robotics. Bautista’s work is notable for its practical applications in education and agriculture, bridging theory and real-world implementation. With a growing citation record, she is recognized for advancing control theory pedagogy and bio-inspired robotics, making her a valuable contributor to engineering education and autonomous systems.