David L. Moreno

Papers

Total Citations

100

H-Index

About

David L. Moreno is a researcher whose work sits at the intersection of intelligent systems, mobile robotics, and computational intelligence. His primary research focus centers on the development and optimization of fuzzy controllers for autonomous robotic navigation, with a particular emphasis on applying evolutionary and machine learning techniques to solve complex control problems. Moreno's most influential contribution, "Design of a fuzzy controller in mobile robotics using genetic algorithms" (2006), has garnered 75 citations, establishing him as a notable voice in the application of genetic algorithms to fuzzy system optimization. This work demonstrated how evolutionary computation could effectively tune fuzzy logic controllers for real-world robotic tasks. Complementing this, his 2005 study on evolutionary learning for wall-following behavior explored how robots could autonomously develop navigation strategies, accumulating 20 citations within the community. Beyond evolutionary approaches, Moreno has also investigated how human-provided advice can accelerate reinforcement learning in robotic systems, reflecting a broader interest in making autonomous agents more practically trainable. Together, his body of work represents a sustained effort to bridge bio-inspired computation with practical robotics, contributing meaningful methodologies to researchers working on adaptive, intelligent robotic systems.