Jorge M. Cruz‐Duarte

About

Jorge M. Cruz‑Duarte is a researcher at the forefront of intelligent robotics and optimization, with key contributions spanning Brain‑Computer Interfaces (BCIs) and warehouse automation. His work on motor imagery classification using a recurrent‑convolutional architecture to control a hexapod robot (31 citations) has advanced real‑time bio‑signal processing for assistive technologies. More recently, he has focused on Robotic Mobile Fulfillment Systems (RMFS), publishing a systematic review (16 citations) and pioneering hyper‑heuristic approaches for the Pod Allocation Problem (PAP). His 2025 study analyzing 20 hyper‑heuristics for PAP demonstrates his commitment to scalable, efficient automation solutions. Cruz‑Duarte’s research is notable for bridging high‑level solvers—including neural networks—with practical warehouse logistics, addressing the computational complexity of RMFS. With a growing citation record and a clear trajectory from BCI‑driven robotics to industrial optimization, his work is shaping the future of autonomous systems in both healthcare and logistics.

Research Focus

Key Achievements

Top Papers

1. 1
   Motor Imagery Classification Based on a Recurrent-Convolutional Architecture to Control a Hexapod Robot

   31 citations · 2021
2. 2
   Robotic Mobile Fulfillment System: A Systematic Review

   16 citations · 2024
3. 3
   Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach

   3 citations · 2025
4. 4
   On the Feasibility of using Neural Networks as High-level solvers for the Pod Allocation Problem within Robotic Mobile Fulfillment Systems

   2 citations · 2023
5. 5
   On the Feasibility of Using a High-Level Solver within Robotic Mobile Fulfillment Systems

   2 citations · 2023

Key Collaborators

Contact & Links