Max Robotics — Global Service Robot Directory + US Certification

About

V. Rodellar’s research bridges artificial intelligence, signal processing, and robotics, with a focus on explainable AI and human-centered computing. Their most impactful contribution is the highly cited 2023 work “Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends,” which has garnered 191 citations and provides a comprehensive synthesis of deep learning’s role in making AI systems transparent and interpretable. Rodellar also explores affective computing through “An ICA-based method for stress classification from voice samples” (9 citations), demonstrating how machine learning can analyze vocal biomarkers for psychological state detection. Earlier foundational work includes “Petri nets for robot lattices” (3 citations), which applies binary Petri-net modeling to optimize flexible robotic production lines in industrial manufacturing. This trajectory—from theoretical modeling of robotic systems to contemporary explainable AI—reflects a sustained commitment to advancing both the interpretability and practical utility of intelligent systems. Rodellar’s work is particularly valuable for researchers interested in the intersection of AI transparency, voice-based emotion recognition, and industrial automation.