Elizabeth Montero

Federico Santa María Technical University

Papers

Total Citations

H-Index

About

Elizabeth Montero is a robotics and artificial intelligence researcher whose work centers on autonomous robot navigation and intelligent path planning algorithms. Her research focuses on developing and applying metaheuristic optimization techniques — particularly simulated annealing — to solve one of robotics' most fundamental challenges: enabling mobile robots to find efficient, collision-free paths through complex environments. Montero's most notable contributions address the robot motion planning problem across varying levels of environmental uncertainty, from partially known to completely unknown settings. Her 2024 paper, "Robots in Partially Known and Unknown Environments: A Simulated Annealing Approach for Re-Planning," has already garnered 4 citations, demonstrating growing interest in her adaptive re-planning strategies. Her earlier 2017 work laid important groundwork by establishing simulated annealing as a fast, effective, and adaptive solution for off-line robot motion planning in dynamic environments. A consistent thread throughout her research is the pursuit of algorithms that are not only computationally efficient but also robust enough to handle real-world unpredictability. With a trajectory spanning nearly a decade of focused contributions, Montero represents an emerging voice in intelligent robotics, offering practical algorithmic solutions with strong implications for autonomous systems and human-robot interaction research.