Ulises Orozco-Rosas
Papers
21
Total Citations
1,189
H-Index
8
About
Ulises Orozco-Rosas is a prominent researcher specializing in autonomous mobile robotics, with a particular focus on path planning algorithms, evolutionary computation, and bio-inspired optimization techniques. His work has fundamentally advanced how mobile robots navigate complex environments, tackling one of robotics' most enduring challenges: generating safe, efficient, and adaptive paths in the presence of both static and dynamic obstacles. Orozco-Rosas is perhaps best known for his development and refinement of potential field-based approaches enhanced by biological and evolutionary principles. His Bacterial Potential Field method (2015, 336 citations) introduced microorganism-inspired behavior to robot navigation, while his Membrane Evolutionary Artificial Potential Field framework (2019, 412 citations) pushed the boundaries further by integrating membrane computing paradigms to evolve optimal path parameters. Together, these works have accumulated nearly 1,150 citations, reflecting their considerable influence on the robotics community. His research has also embraced parallel and GPU-accelerated computing to address real-time navigation demands, and more recently extended into reinforcement learning through his QAPF algorithm (2022, 91 citations), demonstrating his ability to incorporate emerging machine learning techniques. Orozco-Rosas represents a rare blend of theoretical innovation and computational pragmatism, making his contributions essential reading for researchers pursuing intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 7Environment Recognition for Path Generation in Autonomous Mobile Robots9 citations · 2019
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