Paulina Ayala
Papers
7
Total Citations
164
H-Index
6
About
Paulina Ayala is a researcher at the forefront of autonomous robotics, artificial intelligence, and human-robot interaction, with her work spanning mobile robot navigation, teleoperation systems, and intelligent control frameworks. Her most influential contribution, "Autonomous Navigation of Robots: Optimization with DQN" (2023, 54 citations), addresses a critical gap in the field by applying Deep Q-Network reinforcement learning to real-time obstacle avoidance and dynamic trajectory planning — moving beyond the limitations of predefined path systems that dominated earlier research. Complementing this, her highly cited work on virtual reality teleoperation (42 citations) positions her as a key voice in Industry 4.0 automation, exploring how immersive technologies can enhance mobile robot manipulation in manufacturing environments. Ayala's research portfolio also reflects notable breadth, extending into medical rehabilitation technology through her interactive carpal tunnel device and into energy sector robotics with her intelligent oil field systems. Her consistent application of deep reinforcement learning, ROS frameworks, and SLAM-based navigation across multiple domains underscores a unified vision: making autonomous robotic systems smarter, safer, and more adaptable. With over 160 cumulative citations in just a few years, Ayala is emerging as a significant contributor to next-generation intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Navigation of Robots: Optimization with DQN54 citations · 2023
- 2Virtual Reality Teleoperation System for Mobile Robot Manipulation42 citations · 2023
- 3
- 4Interactive Device for Carpal Tunnel Rehabilitation16 citations · 2023
- 5
- 6Applying Deep Q-Networks to Local Route Optimization12 citations · 2024
- 7