Andrea Figueroa

University of Washington, Federico Santa María Technical University

Papers

Total Citations

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About

Andrea Figueroa is a robotics researcher whose work centers on autonomous navigation and intelligent path planning for mobile robots. Her research addresses one of the foundational challenges in robotics: enabling robots to find efficient, collision-free routes through environments ranging from fully mapped to completely unknown terrain. Figueroa has made notable contributions by applying simulated annealing — a probabilistic optimization technique — to robot motion planning, demonstrating its effectiveness both in offline scenarios and in dynamic, partially known environments. Her 2017 paper, "An Effective Simulated Annealing for Off-Line Robot Motion Planning," laid the groundwork for her optimization-based approach, while her 2024 follow-up, "Robots in Partially Known and Unknown Environments: A Simulated Annealing Approach for Re-Planning," extended this framework to real-world complexities where environmental information is incomplete or evolving. Together, these works have accumulated citations that reflect growing interest in adaptive, computationally efficient planning strategies. Figueroa's research is particularly valuable for students and engineers working on autonomous systems, as it bridges classical optimization theory with practical robotics challenges. Her sustained focus on making planning algorithms fast, adaptive, and robust positions her as a meaningful contributor to the field of intelligent mobile robotics.