Miguel Angel Funes‐Lora
Instituto Politécnico Nacional, University of Michigan–Ann Arbor
Papers
4
Total Citations
23
H-Index
3
About
Miguel Angel Funes‐Lora is a researcher dedicated to advancing industrial robotics, with a primary focus on optimizing robotic path generation and trajectory planning for manufacturing applications. His work addresses critical challenges in reducing downtime and manual error in industrial settings. Funes‐Lora’s most cited paper, "A Novel Mesh Following Technique Based on a Non-Approximant Surface Reconstruction for Industrial Robotic Path Generation" (2019, 10 citations), introduces an innovative method for generating robot paths directly from 3D surface models, bypassing traditional approximation errors. He further explores optimization in "Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace" (2018, 7 citations), comparing algorithms to eliminate singularities in recorded trajectories. His 2021 study on surface optimization for trajectory reconstruction (3 citations) proposes a steepest-descent algorithm to refine manually taught toolpaths using a 3D workpiece model. Beyond technical contributions, Funes‐Lora is notable for his educational impact, co-authoring "A Common First-Year Undergraduate Engineering Course in Manufacturing based on Industrial Robots and Flipped Classroom" (2022, 3 citations), which integrates robotics into engineering pedagogy. With a total of 23 citations across his key works, Funes‐Lora’s research bridges theoretical optimization and practical industrial application, while also shaping the next generation of engineers.
Research Focus
Key Achievements
Top Papers
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