Alejandra Molina-Leal
Papers
3
Total Citations
42
H-Index
3
About
Alejandra Molina-Leal is a leading researcher in autonomous mobile robotics, with a focused expertise in developing intelligent navigation systems for dynamic, human-robot collaborative environments. Her primary contributions lie in the application of Long Short-Term Memory (LSTM) neural networks to solve the critical challenge of real-time obstacle avoidance. Molina-Leal’s seminal 2021 work, "Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network," which has garnered 29 citations, established a foundational framework for predicting and reacting to moving obstacles. She has since advanced this research from simulation to reality, with her 2024 papers demonstrating the physical implementation of these LSTM-based algorithms on actual robotic platforms. This progression from theoretical model to practical, collision-free navigation is her hallmark achievement, directly addressing the safety and precision demands of modern industrial human-robot interaction. Her work is pivotal for students and engineers seeking to understand how deep learning can enable robots to operate safely alongside people in complex, unpredictable settings.
Research Focus
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