Mayra Antonio-Cruz

Papers

Total Citations

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About

Mayra Antonio-Cruz is a robotics researcher whose work centers on the control, navigation, and design of wheeled mobile robots (WMRs). Her primary contributions lie in developing sophisticated hierarchical control systems that bridge the gap between theoretical models and real-world hardware. In her most impactful work, "Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems" (31 citations), she proposed a novel three-level controller integrating the dynamics of the robot's mechanical structure, actuators, and power converters, successfully demonstrating its effectiveness on an experimental platform. This holistic approach to trajectory tracking is a hallmark of her research. More recently, Antonio-Cruz has ventured into artificial intelligence, addressing the classic coverage path planning problem with an actor-critic deep reinforcement learning method (2025, 8 citations), showcasing her adaptability to cutting-edge techniques. Her work also includes practical robot design, such as an Ackerman-configuration mobile robot with a manipulator arm, and comprehensive reviews of embedded systems for control tasks. Through her focus on complete system integration—from power electronics to AI-driven planning—Antonio-Cruz is advancing the reliability and autonomy of mobile robots for real-world applications.